{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  PRICE  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if you dont have column name in your csv, add names tag and enter column name and put header = None and DONE\n",
    "data = pd.read_csv(\"housing.csv\", names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO','B', 'LSTAT','PRICE'], header = None)\n",
    "data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.drop(['PRICE'], axis = 1, inplace = True) #without inplace=True it will not give desired output\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #no brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns #no brackets with s in colmun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS       float64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD          int64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "PRICE      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes #with a s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       452\n",
       "ZN          27\n",
       "INDUS       77\n",
       "CHAS        16\n",
       "NOX        132\n",
       "RM         437\n",
       "AGE        399\n",
       "DIS        361\n",
       "RAD         10\n",
       "TAX         67\n",
       "PTRATIO     85\n",
       "B          374\n",
       "LSTAT      445\n",
       "PRICE      210\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique() #identify unique number of values in column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM        0\n",
       "ZN          0\n",
       "INDUS       0\n",
       "CHAS        0\n",
       "NOX         0\n",
       "RM          0\n",
       "AGE         0\n",
       "DIS         0\n",
       "RAD         0\n",
       "TAX         0\n",
       "PTRATIO     0\n",
       "B           0\n",
       "LSTAT       0\n",
       "PRICE      54\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() #brackets in both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT, PRICE]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see rows with missing values\n",
    "data[data.isnull().any(axis=1)].head() #prices has 54 null value and we need to impute the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "      <td>452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.420825</td>\n",
       "      <td>12.721239</td>\n",
       "      <td>10.304889</td>\n",
       "      <td>0.077434</td>\n",
       "      <td>0.540816</td>\n",
       "      <td>6.343538</td>\n",
       "      <td>65.557965</td>\n",
       "      <td>4.043570</td>\n",
       "      <td>7.823009</td>\n",
       "      <td>377.442478</td>\n",
       "      <td>18.247124</td>\n",
       "      <td>369.826504</td>\n",
       "      <td>11.441881</td>\n",
       "      <td>23.750442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.495894</td>\n",
       "      <td>24.326032</td>\n",
       "      <td>6.797103</td>\n",
       "      <td>0.267574</td>\n",
       "      <td>0.113816</td>\n",
       "      <td>0.666808</td>\n",
       "      <td>28.127025</td>\n",
       "      <td>2.090492</td>\n",
       "      <td>7.543494</td>\n",
       "      <td>151.327573</td>\n",
       "      <td>2.200064</td>\n",
       "      <td>68.554439</td>\n",
       "      <td>6.156437</td>\n",
       "      <td>8.808602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>6.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.069875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.447000</td>\n",
       "      <td>5.926750</td>\n",
       "      <td>40.950000</td>\n",
       "      <td>2.354750</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>276.750000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>377.717500</td>\n",
       "      <td>6.587500</td>\n",
       "      <td>18.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.191030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.519000</td>\n",
       "      <td>6.229000</td>\n",
       "      <td>71.800000</td>\n",
       "      <td>3.550400</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>392.080000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>21.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.211460</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>6.635000</td>\n",
       "      <td>91.625000</td>\n",
       "      <td>5.401100</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>411.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.157500</td>\n",
       "      <td>15.105000</td>\n",
       "      <td>26.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.966540</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>34.410000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  452.000000  452.000000  452.000000  452.000000  452.000000  452.000000   \n",
       "mean     1.420825   12.721239   10.304889    0.077434    0.540816    6.343538   \n",
       "std      2.495894   24.326032    6.797103    0.267574    0.113816    0.666808   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.069875    0.000000    4.930000    0.000000    0.447000    5.926750   \n",
       "50%      0.191030    0.000000    8.140000    0.000000    0.519000    6.229000   \n",
       "75%      1.211460   20.000000   18.100000    0.000000    0.605000    6.635000   \n",
       "max      9.966540  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  452.000000  452.000000  452.000000  452.000000  452.000000  452.000000   \n",
       "mean    65.557965    4.043570    7.823009  377.442478   18.247124  369.826504   \n",
       "std     28.127025    2.090492    7.543494  151.327573    2.200064   68.554439   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     40.950000    2.354750    4.000000  276.750000   16.800000  377.717500   \n",
       "50%     71.800000    3.550400    5.000000  307.000000   18.600000  392.080000   \n",
       "75%     91.625000    5.401100    7.000000  411.000000   20.200000  396.157500   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       PRICE  \n",
       "count  452.000000  452.000000  \n",
       "mean    11.441881   23.750442  \n",
       "std      6.156437    8.808602  \n",
       "min      1.730000    6.300000  \n",
       "25%      6.587500   18.500000  \n",
       "50%     10.250000   21.950000  \n",
       "75%     15.105000   26.600000  \n",
       "max     34.410000   50.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find out correlation between the features\n",
    "corr = data.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c932ff4848>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LIzY6kl0HDrs7ZACCb78TwxrAiWcex8zIIGcFWIKCCR34GGljvyq37DFCQgl77ClSPv2AiFeGeSTmkuTk5fPVwhX073oxva84rd7z6sjy6z23dHHZdtZ7TjJj9eb/yYbL/8CqYh2A3aZp7gUwDGMicBNwesPFBE49LQgDDp3vl3rt5HyzrCE9pWjZ/RpSjhwtbLQA/L16Lcf37qNF92sqM7yzsm5fAunZuXRrW/SDCwzw56oWDVi2bd85ny880Eae3f1PrsKuvoqUxUtcGiiJ02fgY7MReknHUtNF3OC8IZyY9JO7QyyTpUlrzJ0bXRoo5oblGP4BGHHe3+VcvUtnEhctcWmgHJ06HZ9AG+GdLik1XdRNNwJw5MfJbo+xPOsSTpCem0e3prGF+wL9fbmqYQzL9pZdEbAYVVea2y6/gqw/fnepJKTPmYXFZsPWvkO56as9/hTZ6/4ia8Wf7gyzXOu27yY9M4vul7Yv3BdoDaDzRW1Y9temKoysbA2u68reBb+5NFC2TP4Zv8BA6l3eqdR00a0u4NBf6wobLQB7Fi7CdDho1P1at8Z8urrXdOXAr4tcGih7fnLGH3PZpaWms/j5Yubnk5dedN3lp2dg5ud7dCC8b4dLyVu93KWBkvfbXAyrDd/WF57TuRypyQAYvn6VGmN51u3cS3pWNt07ti3cF2gNoHO7lizbsLWMlE6HTiQyZuZCXuhzmzvDLMZ62eVkL3ctezLnzsZisxFwUfllT9hjT5Kz/i+yV65wZ5jlKqr3FA1rDwzw56qWDVm2de85ny88yEpe/lkOlZNKZRjGAMMw1pz2Z8AZh9QGDpy2fbBg3+leAe4xDOMgzt6Wx883Lq9tuFREzaaNOLp9Z7H9R7btpGbTxh6PZ9+xRHwsBvWiXMf0x9eszr5jZzfnJt/uIDUzm5lrtvLHjr/p1amNO0J1YWvYgOzde1z25SYcwp6ZibVh6U9hg9u1JXvPXqLuuoM2a1dy0f49NJ81neCLzu2Gd96iYuD4GY36lJOYudnOz7xcYMOGZO7e7bIvJ+EQ9oxMAhuVnv+h7dqSuXsPtXrfxSXr13Blwt9cOGcmoe0vcnfIxexLTMXHMKhXzXVYQHxEKPsSU0tJVfX84+LJ2+d6c7UfPowjMxO/uPiy0zZuQkjPWzj5/ttlHucJexMO42OxUK+W65C1+Dq12JdQ/hPkPQcOc9Hdj9Dq9gH0fuFNVm3e4a5QXUQ2bsSJnbtc9qUeSCA3I4PIJqWX4b7WAOy5eS77HPn5mA5HmekqW3jjhiSfEX/6wQTyMjIIb9yo1HT7pv9CfmYWl7z5GtbISKyRkVzy9hvkJCezd9oMd4ddyFI3Dsf+v132mceOYGZlYakbV/4JDAN8fbHE1sPa/0nyt23Gvn2ze4Itxd5DRwuufdchU/G1o9l36Gi56d8dP5VuHdvSIi623GMrk19cPHn7XB9o2o8cxpGViV/9svPer1Fjgm66mZQPyx5K7An7jpZS74mOqEC9Zwt/bP+bXpe6v94jxZmmOco0zYtO+zPqjENKeqxyZq/DXcBY0zTrANcD3xmGcV5tjzKHihmGUeZ4AtM0fz6fL69sgdXCyUxOKbY/MymZyPj6Ho8nJTObQH9/fCyu/0ZhNitZuXnk5tvx9/UpNf2Gvw9x18ffA+BrsTDk1i50bVX6za+y+ISFkZ9avHJpT07BN7z0sfF+NaKwNogn5qknOPDGm+QnJVHrkYE0mfAtGzpdSf4JD03StAVhZmcW35+VgWELKvar8ja+4WHkpxTP/7yUFPzCwktNF1AjisCGDaj/9BPseW0YeYlJ1H3sEVpPHM+KjpeRd9xzk2RTsvMI9Pctfu1b/cjKs5Nrt+PvU/q1X1UsoaHY04rnvSM1FUto2fNCqr/4EqkTvyd//358Y8586ORZqekZBFoD8PFxzf/Q4CCycnLJzcvH36/k4r9ZXF1aNY6nQZ0YklLT+Gb6PB589X3GD3uBVo3LbrydL2u1cLJLKMOzk1KwljEvJ3HPPlrecSsWX18c+fkA1GrXBouvL9aIsheDqEz+4eHkphSPPyc5hYAyys7MI0eY2aMn3SZP4IJHHgIg4/ARZvXsRfaJk26L90xGSAhmevHheGZ6KkZI+XNDAt/+HL8Ozp6l/B1byHz+sbIXRHGD1Iws57V/RtkTGhTovPbz8/EvZc7lyi07+X3jduZ8+JInQnVhCQnFUcGyJ/z5l0ifOIH8A/vxqeKyJyUrm8CAEuo9gedQ7/loPFBQ77mtq0fqPVXB8t+/rthB4PQWfh2KDwXrB3QDME3zT8MwrEAkcKyiX1reHJefgPUFf8C1dWUCJTZcCrqTBgCMHDmyorFVTAmFpGEYbi88TdPE7ij6jlOjXUoa9WIWVJ3LGxHTqFYkk565l9SsbJZu3cuwKb8SbA2gx4UeGO5UUn6Vl4+GBZ/gYHYPeJiURUsASFu9ljar/yT6gT4kvPuBm4ItQYlxGhR/GOClSryOyxlCabHgGxzMln4PkbhoMQApq9dwyV8rqfPA/ex75z03hWpiPy0u44z/uhxb+JkXF9gVuPaDuvfALy6OI48+5MbASuYse4qGUpzKW6OEAubU9VNW2XPfDa7Daq+8sBX/emIoo6bM4vMXzruXv3wl5n8p+wus+/pbLn7sIbp99A5L3niHwIgIrv/kPWeviweG156upN+oYRhl/nYDo6O55ruvObF+A0sfewqAFgP60f2nH5jetTvpBz0516uUsvMs7qHZn75DTmgoltp1Cbi3P0HvfEH6Y30hL7fSo4QSrn3j1LVf8rFQetmTb7czbNxPDOx53VlN4HeLCpQ9tm7X41e/PieeGOjGwEpWar2nxGNdjylNo1qRTBp0L6lZOSzdsodhPy0k2OpPjwubV07QUplWA40Mw4gDEoA7gbvPOGY/0AUYaxhGM8AKHD+fLy2v4XIrcAfQCpgO/GCa5u6ykzi7l4BTXUrmwIeePZ8Yz1pmUjLBUcVXvrGFh5XYE1OZVu8+QN8vfizcbt8glm5tm5CRk4vd4XB5+pCalYPN3w+/cp44Bwb407JuTQA6NalPWlYOH8xc4vaGiz0lBZ/Q4gW3T2hIiT0Bp+QnO8c0py4vGmPrSE8nY+MmbGUMk6h0WRlgCyq+3xqImVVCT4yXyU9OwTespPwPJT+19Os4L8mZ/8nLi+ZX2NPTSdu4icAm7sv/1QeO03fiosLt9rFRdGsaS0ZufvFrPzsPm58Pfj7eOUrVkZqKpYQny5aQ4BKfhgLg60v1Qf8mZcxoDIsFIyQES7BziJxhC8QIDMIsZVJzZVi9ZQd9XioaItK+RRO6XdqejKxs7HaHS69LWkYmtgB//M5hlT9rgD9XtLuAxWs2lH/wecpOSsZaQs+ENSyU7OTSy56TO3fxy6NPc+27w7iw//047Hb+GjMO0zTJOFbhB3vnLDc5mYCw4vH7h4YUTrovSeunHsPi68uCe+4v7DFKWLKMO9evotUTj7L83y+6LebTmWlpGEHFV9IygoNL7Ik5kyNhPySAfdtm8jetI2TCLPy6didvznR3hMvqbbvp8/qnhdvtmzWkW8e2ZGTlFCt70jKzCq79ku+7k39bTlpGFj2v6EBqhvM+kZdvx+EwSc3IxBYQUGrayuBIK6XsCS677Al/ejCp33wFp8qeIOe9z7DZ3F/27D5A388nFm63bxhLtzZNS6n3ZJ9Dvce57HqnJvVJy87lgxlL1HDxQqZp5huG8RgwD+dSx1+bprnFMIzXgDWmac4ABgGjDcN4GudTkb5mRSaxn6bMu5dpmlOBqYZhBOFcKeADwzCqA0NM01xyPl/sDke27+KyEiZw1mzamPXTfnHrd7eIrcmkZ4qW3QwK8OdoShp2h8n+48nERUcUfrbvaCJxNSJKOk2ZmsdGM3XVZvLs9nJ//Ocja/cebGfMZfGPqYVPUFCxuS+ny9612/mugjOfqBgGODzY03H8EEZUjOtzw7AIjABr8bkvXihz924CGzZ02RcQE4NvUBCZu0rP/8xduwry3/UfwMC9+d8iuhqT7i16Sh/k78vR9Czspsn+pHTiqhfdjPclphIXUUVPM89C7r69+Me7DofyqVkTS2BQsbkvp1hsNnxr1aL6cy9S/TnXCmb0Bx+Tt/8fDrhxcZAWDeox+b2ioS1BNitHTyZjdzjYf+QocbWL3r2yN+Gwy/Y58cCiCSd27qL6GY3s0Dq18Q8O5sSO4vMXT7dh3PdsnvgT1RvGk3H8BJknTvLsoT2s++Y7d4bsInnn7mJzWYJqx+AXHFxs7svpwhs1JHH7jsJGC4AjL4+kbdsJjT+LuSWVxLF/H5a69V32GVHRGLZAHPvPbUEZ8+hhzLQULLXO/t0p56pFXCyT3xhcuB1kC+Bo4qlr/zhxMUVzvPYeOuqyfaZ9h45xJDGZyx8eUuyzix98jnceuY8bL29fQsrKkbdvL75nzKPziS4oe/4uOe8Nmw3fmrWoNvgFqg1+weWzyHc/Im//Pxy54Tq3xdwiNppJg86s96QX1HuSiIsuWg22wvWeOtFMXbnJ7fUeqZiCd7LMPmPfy6f9/1ag9JVJKuBsH7tlAylAKlAXZ1eP19k8ZwE9Xn6OBpd2ZM8fzqf+dS9sS1SDOLbMWeDW7w6yFvWOnBITEUqw1Z95G3Yw8FrnalBZuXks3rKH2y9pdc7fsW5vAjXDQ9z+4035bTG1Hn4IS1AQjoIVTiJuvAF7Vhapf5a+Yknywl+p/ezThHbqRMpvzifwPiEhBLVqyZERZ87pch/Hjg1YrvgX+FshNxsAo9UlmLk5mPu2eSyOijr56yLqPjoQn6Ag7AX5X6PnDdgzs1x6U4qlm7+QuMGDCL+0E4m//gY48z+49QUc+NJ9QzaDAvxoWcv1hhQTFkSwvx/zdhxgYKcWAGTl5bN4zyFub+35ZXbPVtaypYQ90M/lSWVwt+txZGWRtXpViWkcmZkc6nuPyz6fyCii3/+IxI8+IGule1cYC7LZaNnQtXIbExVJcKCNucvX8PDtNwCQlZPD4tUbuP3aK8/p/Nk5uSxbt4kW8fUqLebS7Jm3kEuefhz/4GByC1bVa37bzeRlZvLPaatFlsaek8OxLc7feKt77sSwWNg6ZZpbYz7d/gULaf3EY/gFB5NXEH+DW53xH/q9+Es9T0k7cJC613bF4ueHI8+5yIDF359qzZux34PLyeev+gP/O/qALRAKeqf9Ol+HmZ1F/oa153QuS2w9LGHVcBxx3zC3IJuVlg1cX6gaExlBsM3K3BXrePgW5/vbsnJyWfzXZm6/uvT6U+/rrqBre9f78ujpCzh4/CSvPngn8WU0eipD9u/LCOn7gEvZE3ids+zJWVNy2WNmZnKs330u+yyRkUS+8yHJn3xIzmr3rjAWZA0o7B05JSYizFnvWb+Dgdc5HyQX1ns6tT7n71i376BH6j1V4X9gOeQqUd7k/M44VwToACwEPjFNc40nAvOz2bjgeucyluG1Y7CGhtDu1psA2DR7PnlZWby2az27lvzOdw8+BsC+FavYMnchfb8dyZRnhxa+gHLXsuUef4cLQICfLw92uZgR8/8k1BZAXHR1xi1eg8M0C9c3B5i+ajNDJ85l7tD+1I4IIyExhaE/zKVHu2bUqR5GZm4eCzfuYva67bx8u/uXdT727Xii+91PozGjOPzFcALq1aX2s09zZORolyWSWy1fStqfK9g3yPlW4YwNG0maO4+4D9/j4LC3yUtMpNajAzHz8zn6zbduj/sUc+VC6HQdlnuewVwyAyJqYOlyG+bvs12WSPZ59iPMvdtw/FzUqDIatwb/AIxazkqa0dK5DKV5cC8ke2Zy+6Fx31Gn/wO0HPsV+z/7Emu9utQfPIgDI0a5LJF88crfSV6+gh1PO4dipm3YyPE5c2n68fvsfeMt8k4mUvexhzHz8kn4eqxHYj8lwNeHBzs2ZcTyrYRa/YmrHsq41Tuc1367oifS0zfvY+ic1cwd0IPaYc4hDgkpGWw+4lx9Js/uYM/JVObtOIDNz5cr4t375vbUST8Qes+9RH/yOSljRuMbG0u1Rx8n5dtvXJYpjZ2zgKzVqzjx8hCw28k+o1FzanJ+7q4d5Gza6NaYSxLg70f/W3zsiP8AACAASURBVK5n+KSZhAYFEl+nFmNnzMdhmtzTo+hdCdMW/cHQz79h3vC3qV0jkrSMTB4e9gk3XHkJdWvVICk1nXEz53P0ZDIfPfuw2+NeO/ob2j8ygNt//JblH3xCeFx9rhz6b1Z8+qXLEsmPblnDP8uW88vAJwDwDwnh8uee4Z/fl+PIt1P/ysvo+NSj/PLIU2QXDKH0hK1jxtJy4ACu/X4s6z/6lNC4+lz0wmA2fT7cZYnkO9ev4vAfy1nyqHM+y/Zx42na5x6u/eFbto7+GgyDFv0fILBmNFs9WHbmzJiM/y13Efjah+T88I3zBZR9B5IzebzLEsnB42dg37CWrPdeBcA68GlMux37tk2Y6Wn41Isn4M6+2BP2k/ebZ98jFeDvR/8br2H41LmEBgcSHxPN2Fm/4XCY3HPdFYXHTVu6kqEjJzDv4/9QOyqCejWjqFczyuVcU5esICktnQ7N3T/UOX3yRELuvofIDz8l9Zuv8K0TS+jDj5L23ViXsqfmzHnkrF1N0itDwW4v1qg5NTk/b/dOcqui7PHz5cGuHRkxbzmhgVbialRn3OLVzrL/8jPqPT/MYe5LA4rqPRPm0OPCZtSpHk5mTq6z3vOXZ+o98t+jvB6XX4GNwO9AAHCfYRiFzXvTNJ9wV2ChNaIY8JNrF/+p7SH1W3Lyn/34+PpgOaMV/tWd93P7R29x39dfYFgsbPplHj8+MZiq0r/rxThMk9ELV5KcmU2L2Gi+evh2IkOK5mA4TJwT3ApG84TarESFBjFi/p+cSMsgxBZAw5qRDB9wK1c2d++qPuCc47K9113UH/Yajcd9TX5qKkdGfUXC+x+5HGf4+sAZ+b/n0SeJfXkIdV95CYvNRtrqNWy/7U7sJay04zbZGdjHDMNy4/1Y+gyGrAzMP2bjWHjG+2UsPnDGyieWnv0wqhXdvHx6Pw2AffJwzL+Wuj10gPyUFNbfegeN3xrGBd99Q35qKgdHjGbfe66LGxg+vhhn5P+2Rx6nwX9eouGr/8Fis5Kyeg3rb+1Fvifzv0D/i5vhMGH0im0kZ+fSomY1vup1FZFBRR22DhOXif0Aq/YfY8icopvxvB0HmLfjADGhgSwceINbY3akpnK4Xx8ih7xM9BcjcKSlkvLtWJK++Mz1QB+fYnnvbfrfcj0Oh4PRP88mOS2dlg3qM+aVQUSeNofkzMnN/n5+VAsNYcTkmZxMSSPA3482TRrw7RvPFevVcYfs5BTGd7+Zbh+/wx1TJpCdnMLKz0aw5HXXJaYtvr5YTpu7Y9rtRLdpRdsH7sPXZuX4lm1Muft+dsycfeZXuFVucgq//OtmLvvgHbpN+p6clFQ2fjGCtW+6LlNr8XX97Z5Yv4E5N/fiwhf+TedRXwKQuHUbs268jcTNWzz3F0hPI2PQQ9ieeJ6gNz/BTE8jZ/J4csaNcDnM8PF1lp8F7Du24n/LXfj/61YMf38cx46Qt3QhOd9/DdnZnou/QP+brnHed6cvIDktg5bxsYx58VEiT5t0X3Tte8eCLWZaKscG3E+1F14i8tPhmGmppI0fR+rwz12OM3x8MSxeXvacqvcsWFFQ76nJV4/0IjL09HqPWUK9J9hZ70nNIMRmpWHN6s56Twvv7aUXzzPKmiNjGEZfyvhVm6Y57iy+wxxoeO+Y9rKMMJ0T4uxzvqriSCrGp/uDAKyqVbecI71Th8P7yX/hrqoOo8J83/qBRTWqdmnK89H5WAL2MS+Xf6AX8un3GgB7W3j+/U2VIX6Lcz6HY2vpw4u8maW5c0jO69ZzH9PuDV7KTmRkSPGFXv5bPJR2gpTO/73vvghbtB7HX/OrOowKsbRzjhQ50LppOUd6p9gN2wGwzx1TxZFUjE+3fuDR17ZW3ITwGt7Rai7F3cnHvDIfy5ucP9ZDcYiIiIiIiJSqvDkuMym7x+XGSo9IRERERETkDOXNcXnfI1GIiIiIiPw/4ZXjsP4LlDdUrNR3tRiGUanrMouIiIiIiJSmvKFiPkAvoDYw1zTNzYZh/At4EbABbd0fooiIiIiI/H9X3lCxMUAssAr41DCMf4BLgOdN0/TcW71ERERERP5HWAwNFquI8houFwGtTNN0GIZhBU4ADU3TPOL+0ERERERERJws5Xyea5qmA8A0zWxgpxotIiIiIiLiaeX1uDQ1DGNjwf8bQIPTtjFNs5XbIhMRERER+R+kgWIVU17DpTUQDRw4Y3894JBbIhIRERERETlDeUPFPgJSTdP85/Q/QGbBZyIiIiIiIm5XXsOlvmmaG8/caZrmGqC+WyISERERERE5Q3lDxaxlfGarzEBERERERP4/0ByXiimvx2W1YRj9z9xpGEY/YK17QhIREREREXFVXo/LU8BUwzB6U9RQuQjwB252Z2AiIiIiIiKnlNlwMU3zKNDJMIzOQMuC3bNM0/zN7ZGJiIiIiPwP0lCxijFM03T3d7j9C0RERERE+C9pE/xULdqr68e3JR31ynwsb46LiIiIiIhIlStvjkulsM/5yhNfU+l8uj8IwEAjtIojqZgRZioA5t8bqjiSijHqt8b+4RNVHUaF+TzzKeaev6o6jAozGrQjtWu7qg6jQkIXOvPdPLi9iiOpGKNOUwAWRtWu4kgqpuvxBADsv4ys4kgqxudfD2GfNaqqw6gwnx4DcCyfWtVhVJil083kDehW1WFUiN+ouQA41s6r4kgqxnLhdQCkXNW6iiOpmLDF/z31HcPwyg4Nr6ceFxERERER8XpquIiIiIiIiNfzyFAxERERERFx0kCxilGPi4iIiIiIeD01XERERERExOtpqJiIiIiIiAep56BilG8iIiIiIuL11HARERERERGvp6FiIiIiIiIepPdPVox6XERERERExOup4SIiIiIiIl5PDRcREREREfF6muMiIiIiIuJBBprkUhHqcREREREREa+nhouIiIiIiHg9rx8qZpomoxau5Mc/1pOUkUXL2Jq8eMvVNKsTXWa6z+b8zsKNuziUmIqJSVyNCB7o3IHu7Zp6JO6oBvFcM/gJ4ju2J6Zlc3YvW86HnXuUm84aGkqvj9+mdc8eWCwWNv0yjx+f+DcZiYkeiLo40zQZOXEqE39ZQFJqKhc0bsiQR+6nWYP6ZaZ7/v0vmLZgSbH9s0d/RHzd2m6KtmRGh2swWl8GtiA4sh/HoilwPKHsRHWbYLTsiFGrPkZYdRx/zsH8c45nAj6NaZqMnDSdibMWkJSaxgWNGjBkYJ/y8//D4UxbuLTY/tkj3yc+1r35b6kbh/Wx5/BpfgFmejp5c6aS890ocDhKT1MvHuvAZ7DENcIIDcNMPkn+mhXkjB2OmXjCrfGWxjRNRk74iYkz55CUksYFTRoy5LH+NGsYX27apJRUPhoznt+WryQtI5OY6Cgeuvs2el57tdvjDmrciCZvvUHYRReSn5pCwvgf2Pveh2Xmf/zgZ4j/96ASP9v9xlv8/cnn7gq3VKZpMurXVfy4fIOz7K9bkxd7dqZZ7Rplpvts7nIWbtrFoaQ0Z9kfFcEDV11E97ZNPBS5U1H860lKz6Zl3WhevPnqs4j/DxZu3M2hpNSi+DtfRPe2nrl3nWKaJqNmLWbibytISs+gZVwdhvS+kWZ1Y876HAv/2sLjn31Hi/q1+ek/j7sx2gK16uJz58MYDZpBZgaO3+fimPk9mKVf+0a9xliu+hdGo5YQHgGJJ3CsWoRj7iTIz3N/zCUwTZNR0xcwceHvJKVl0LJBXYbcdyvN6tc563MsXL2Rxz/6ihZxsfw0bLAboy1iqReP7Ynn8WnRCjM9jdxZU8kZN6Lssr9+A6yPDMInvhFGaDhm0kny1/xJ9pgvqqzs9xQNFKsYr2+4jF64khHz/+TZG68krkYE4xavod/wyUx/ri9RocGlpsvIzqVnh5Y0iK6Oj8Vg/oadDPp2JhaLwXVt3H8Di2nRlJbXX8u+Favx8fc/63T9fxxLdJOGjH/wcUyHg5vfeY2B0ybwwRXd3Bht6Ub9OI3hE6Yw+MF7iY+NYezPs7j/+deZOfIDoiLCy0wbH1ubNwc97LKvds0od4ZbjNH+GoyO12EunY6ZeBTLhZ2x3PYojnFvQWZa6enqN8OIjME8sBMC23ku4DOMmjSd4T/8zOB+vYmvE8PYqbO5f8gwZn753lnkfwxvPj3QZV/taDfnf3AIge+OwLF/L5kvP4Mlpg7Wh54Bi4Wcb74sNZkRFIzjSAK5C37BPHkcS83aBNw7AJ/Gzch45F5w2N0bdwlG/TCF4eMnMfihvsTH1mbsTzO4f/DLzBzzGVER1UpNl56Ryb1Pv0igzcrQxwcQHhrCnn8OkJeX7/aYfcPCaDdlIuk7drHhvvux1a9P41dfxrBY2PPWu6WmSxj/Ayd+W+yyr8b111H/icc48esiN0ddstG/rWbEghU8e8MVzrJ/yVr6jfiJ6YP7EBUaVGq6jJxcerZvUVT2b9zFoPGznGV/68aei//XVYyYXxB/dATjFq+l34jJTB/ct+z4s3Pp2aGF673ru1lYLBbPxj9rMcNn/MrgXtcTVyuKsfN+54H3vmLGG08TFRZSbvqcvDzemTiLyDLu05UqMBjfp9/CPLwf+xevYkTVwnL7ADAsOKaPKzWZ0f4KiKqFfe4kOHYIo04clpvuw6gTh33EG56J/QyjZyxg+NR5DL77JuJiohk7exEPvPkFM959gajw0HLT5+Tm8c74qUSexb9TpQkOIeiDkTj+2UvmkKew1I7F+vAgsBjkjPmi1GRGUDCOwwnkzZuJo6Dst/Z9iKDGzUkfeDfYPV/2i3fz6oZLTl4+X/26kv5dLqb35c7KY5v6MVzz2igmLFvHkz0uLzXt8ze7Ptm8tGkcu4+cYMbqLR5puGycOYcNM2YDMGDytwRHVi83TVzHDrTo1pX3r+jG7mXLAUhOOMzzqxbRtMtVbP91sTtDLiYnN5fRP05jwB03c89NzoZTm+aN6XLfo3w/Yy5P9b2zzPQ2awBtmnnuRluMjy9Gh66YqxZirl8GgOPQ31gefAWjzRWYy2eVmtRcOh1z6TQAjAYXeCLaYnJycxk9eQYDet3EPTdcB0CbZo3o0vcJvp85j6f63FFmeltAAG2aNvJEqIX8b7gNIyCAzFeehcwM7H+txAgMJuC+AeT8OA4yM0pMZ9+6EfvWjUXbG9biOH6UoHeHY4lvhGP3dk/9FYCCvJ84hQF33co9PZ09pW2aN6VL7/58P20WTz1wT6lpR06YTG5eHj8N/wBrQAAAHdu28kjcdfrei8VqZWPfB7Gnp8OSZfiGBBM/eBB/f/alc18Jcg4fJufwYZd98YOeImPnLtI3b/FE6K7x5OXz1W+r6N+lA70vawtAm3oxXDPsKyb8sZ4nu19aatrnb7rKZfvSJvXZfeQkM9Zs9VjF3yX+y0+L/43RTPh9HU9ef1mpaZ/v2dlluyj+LR6MP4/RsxfTv8dV9O7aCYA2DevR9dl3+H7hcp669bpyzzFmzlKiw0OJrRHBroSj7g4ZyxU9wM8f+/DXITsTc9s6sAViueEeHPMmQ3ZmiekccydDekrhtrlzI2ZeLr73Pok9ogYkHnN77KfLyc1j9IyF9L+pK72vuwKANo3q0/XJV/h+/lKe6vWvcs8x5pdfiY4IIzY6kl0HDpd7fGUIuPF2jAArGS894yzn166AwCCsfQeS88PY0sv+LRuwb9lQtM0azONHCfpgpLPs3+XZsl+8n1fPcVm3L4H07Fy6ndbFHxjgz1UtGrBs275zPl94oI08D7XeTdM85zQtu19DypGjhY0WgL9Xr+X43n206H5NZYZ3VtZt3Ul6Zhbdr7ikcF+g1Urniy9k6ep1Ho/nnMXEYQTYMHf8VbQvPxdz72aMuOblJD73f7/KVpj/l3cs3OfM/3YsXbuhjJRVx7f9peSv+dPlJpW3eB6G1YZvqwvP6VxmqrMyYfj5VWqMZ2Pdlu2kZ2TS/aqiCmagzUrnS9qzdNVfZaSEn+f+yq3drylstHhS9S6dObloiUsD5cjU6fgE2qjW6ZIyUrryDQ8n4srLOfLzNHeEWa51fx9ylv2tTy/7/biqeTzLtlek7Ld6rOyH0+JvU9TQCAzw46oWFYw/yEpefunDbSrbul3/kJ6VQ/cORQ3uwAB/OrdpyrJNO8tNf+hkMmPmLOGFu29wZ5gujJYXYW5d69JAcaxeguFvxWhcxsOn0xothfbvcZ4zpOxebXdYt2sf6VnZdL+4qKc/0BpA53YtWbZ+a7npD51IZMwvv/LCfbe6M8xifC++jLzVy13L/t/mOsv+1hed07kcqclA1ZT9nmR4+R9v5dUNl33HEvGxGNSLch2WEV+zOvuOnd2cj3y7g9TMbGau2cofO/6mV6c27gi1UtRs2oij24vfFI5s20nNpp7vudh7IAEfi4V6tWu57G9Qtw77DhwqN/2e/Qe58OY+XPCvu7n7mZdYtbH8QrcyGRHRmA47JB93/SDxCESUPc7cG+w9eMiZ/zFn5H9s7bPL/wMJXHjrA1xw473c/ewrrNrk/vy3xNbHceBvl33msSOYWVlY6tYv/wSGAb6+WOrUw/rg49i3b8a+fbNbYi3L3v0Hy7j2D5aa7uDho5xMTiE0OIgBL7zGBdfdyiW33MtbX44hN8/94+WDGjYkc9dul305CYewZ2QS1KjBWZ8n+oYeWPz9OTp1RmWHeFaKyn7XimN8dMS5lf1Z2cxcu40/dv5Dr0tauyPUEpV676pxjveuU/Hv+IdenTzTawew98hx5/UfHemyPz6mBvsOl98D8e7EWXRr34oW9T03n9GoGYt55IzfZuJxzJxsjJqx53auBs0wHXbMo6X/1t1l76Gjzryv5TqsNz6m5tnl/fhpdOvYlhZx5/Z3Pl+WunE49rs2yitU9sfWwzrgKfK3bca+zfNlv3g/rx4qlpKZTaC/Pz4W1/ZVmM1KVm4eufl2/H19Sk2/4e9D3PXx9wD4WiwMubULXVt5dujMuQisFk5mcvGnP5lJyUTG1/d4PKlpGQTarPj4uOZ/aHAQWTk55Obl4+9X8iXUvEEcrZs0okG9OiSmpPLNlJn0e+F1vv/gdVo1beiJ8CEgEHJz4Mzer+wsDL8AsPhUydyJs5Wafh75H1+f1k0a0qBubRJT0vjm51n0G/Im37/3Cq2auC//jZAQzPTic4fM9FSM4PLHZge++Rm+7Z1DU+w7tpI55PHi/34ekJqeTqDNho+Pa/kSGhxMVnYOuXl5+JfwNPB4YhIA740aS4/OlzP67f+wfc8+PhozHl8fHwY/1NetcfuGh5GXmlpsf15KCr7hZ//0OPrmm0jdsJHMvXsrM7yzlpKVU0bZn19+2f/PIe76dCJQUPbfcjVdL/BQuUMZ967AgLOL/+9D3PXpD8Dp8Xvu3pWakUVgQPH4QwNtBffefPx9Sy57Vm7bw++bdzLn7Wc9EWqRoGDILGEoZGYaBJ7DPJvQaliuvwtzxW+lDi9zp9SMTAKtAcXzPthGVk5u2Xm/ZSe/b9rGnA9e8kSoLsos+0POoux/5wv8OjiHgObv2ELmc49VSdkv3q/MhothGPeV9blpmt9WViCmaWJ3FF2khuH6X5djC4bxlPTZ6RrVimTSM/eSmpXN0q17GTblV4KtAfS4sFllhV35SvihGobh9h+wM/+LhiKcejGSUUImnxoGV1b+33fz9S7bV3VoR4/+TzNy4s988cq/KyHiEhinF/Rl5JdxFsd42Dnl/1lc//f17O6yfVX7tvQY+Cwjf5zOFy+XvHpUpSnpWj3Lazj783cwQsKw1K6Lf+9+BL75ORlP3g95uW4I1Kl43hf8t8Sy59RnJWf+qd9Go/p1eX3QY4BzfktGZhYjJ/zEY33uwmZ18xCyEvO/lP0l8I+uQbVOHdn12rDKjasUbin7a0Yx6am7Sc3KYem2fQz7+TeCA/zp4YZVJc8pfrP0z07XqFYUk57u7Yx/615n/FZ/erSr/HtXsevfOFX2lHDsqWNKGUiSb7cz7PsZDLzh6rOawF/pSrzGDc66rPfxxWfAi5CThX3SyMqMrESl5n2JxxYcU1befzuFgT2vO6sJ/G5RWv6fTdn/ydvkhIZhqVOXgHv7E/TOF6Q/3gdy3Vf2VzWLN4/H8mLl9bi0L2GfAdwA1AZKbLgYhjEAGAAwcuRI+sWWPyJt9e4D9P3ix6IvbhBLt7ZNyMjJxe5wuDx9SM3Kwebvh59P6U+swDkmt2XdmgB0alKftKwcPpi5xGsbLplJyQRHRRbbbwsPK7EnpjKt2riVPv9+tXC7favmdL/iEjIys7DbHS5P/dMyMrEFBOBXylOfklgD/LmyQ1sWrVhbqXEXqtMQn15PFG6aB3Zh7lwH/gHFK80BNsy8nDKXaPS0VZu20ef51wu321/QjO6Xdyw5/9MrmP8XtWFROfMzzpeZlgbBxSssRlAwZkbpq7id4kg4ABzAvn0z+ZvWETx+Jn5dupM3d7obonVatWEzfQYNLdxu37ol3a+8tCDv7S69Lmnp6disped9WIjzyW6HNq5j6ju2bcVn435g/6HDNHFj72l+cgq+ocUrLb6hoeSnnF0ZEn3TDWAYHJ02s7LDK9HqPQfpO3xy4Xb7BnXo1rpxGWW/71mU/X60jC0o+xvXc5b9s5a5peGyes9B+n45yTX+NmXduyoQf3YOH/yyzC0Nl9U79tLnndFF8TeJo1uHVmRkF48/LTPLee8tpbdo8pLVpGVm0/PSdqRmZgGQl2/H4XCQmpmFzd+/1LTnLSO95J4VW1CpE8PP5PPAsxgx9ch/95mSe28q2eptu+nzxmeF2+2bNaRbx7ZkZOcUz/uMLGwBpeff5N+Wk5aRRc8rOpCa4ewpKsz7wnu2m/IeZ9lvlFT2BweX2BNzJkfCfkgA+7ZN5G/8i5AfZuPX5Xry5lTNPDvxXmXWfEzTLFx43XA+CugNPAesAEp9HGea5ihg1KlN+5yvyg2kRWxNJj1zb+F2UIA/R1PSsDtM9h9PJi46ovCzfUcTiasRUdJpytQ8NpqpqzaTZ7eXe+OoCke27+KyyzsV21+zaWPWT/vFrd/dolE8kz97q3A7yGbj2MlE7A4H/xw6Qnxs0dr9ew8kEBd79mv5n660J9Xn7egB7N+/V7SdmwPBYVgsPhAeBUmnjQ2OiPb4SjHladEwjskfFy29GRRo49iJgvw/fIT4Oqfl/8FDFc9/N0+5cxz4G5/Y+q7fGRWNYQvEsf/vczqXeewwZloKllruHSffonEDJn/5fuG2S94fOkx8bNG7E/buTyAutvR3KcTG1MSvhOF7p3piLBb3TivM2L2boEauQ6ICYmLwDQoiY9eeszpHdM+bSF65ipxD5c+jqgwt6kQz6am7C7edZX+6s+w/kexS1u87VsGyv04Npq7e4payv0WdaCY93btw2y3x145m6io3xV+vDpNffqxwO8gawNGkFOwOB/uPniTutLkWew8fJ65W6fMD9x05zpGkFC5/qnj14OJHX+Wd/ndwY6e2lRr/KeaRA8XnslSLxLDaMI8cKDe9pddDGK0vwf7xi3DmXBk3aREXy+Q3iobUueT9kePExRS9r27v4aNl5/3hYxxJTObyh4cW++zi/s/zziP3cuNlJT2LrhyO/fuw1I1z2Vfhsv9oQdkf49l3vsl/h3If2RqG4Qv0BQYBK4HbTNPcUdmBBFmLekdOiYkIJdjqz7wNOxh4rXNFnKzcPBZv2cPtl5z7RMV1exOoGR7ilY0WgM1zFtDj5edocGlH9vyxAoC6F7YlqkEcW+YscOt3BwfauKCx6+Td2tGRBAfamLfsTx6+27lCSVZ2DotWrKXX9V3P6fzZObksXb2eFo3Kf3lfheTlwNEzbk6piZg5WRiN22CunO/c5+uHEd8Sc+Py4ueoQiXmf41T+b+Ch++6BSjI/5V/0avbub3IMDsnl6VrN9CiUVz5B5+H/NV/EHD7fWALhCznUz+/q67FzM4if+O59bZZ6tTDElYNx+FyXhZ6noIDA7mgiev8gdrRNQgOCmTekuU8fE8v4NS1v5pePa4t9Vz+fn50ateGles2uuz/c91GbNaAYgstVLaTvy6i3qMD8QkKwp7hfMoc3fMG7JlZJC3/s9z01tg6hLe/kG2DX3BrnKcLsvoX9i6cElPtVNm/k4HXOFfVy8rNY/HWvdzesQJl/75D1AwLdkvZf27x76lY/H8nUDPcTfHbAmgZ59oYj4kMJ9gWwNzVG3n4xi4AZOXksnj9Nm6/skOp5+rd5RK6tnNdsXH0rCUcPJHIq31uJr6Mivf5MjevwXLdbRBggxxnb4/loisxc7Mxd24qM62l2x1Yrr4R+6g3MXd7bvnvIJuVlvF1XfbFREYQbLMyd+V6Hr7Zuex0Vk4ui//azO1XF3+weUrva6+g60Wu19boGQs4eDyRV/vdQXztsl/afb7yV/6O/519Xcv+q69zlv0b1pzTuSyxp8p+zzw8qSrufpD4v6q8OS6PAk8CvwLdTNP8xyNRFQjw8+XBLhczYv6fhNoCiIuuzrjFa3CYJr2vKFoqcPqqzQydOJe5Q/tTOyKMhMQUhv4wlx7tmlGnehiZuXks3LiL2eu28/LtnllW2M9m44LrnRWc8NoxWENDaHfrTQBsmj2fvKwsXtu1nl1Lfue7B51Pu/atWMWWuQvp++1Ipjw7tPAFlLuWLff4O1wAAvz96X9HT4ZPmEJocJDzJXw//4LDNAvf6wIwbcEShnw4nPljP6N2dBRpGZkMfOltbuhyOfViapKUksa4qbM4ejKRj4Y87bm/gD0fc9VCjI7XQXZW4QsoMQzM9UVvlTeatce47m4cY16DNOfkakKqQc16zv+3+EJETWjUxtlA+nubR8IP8Pen/+03MnziVEKDg50vAJ06C4fp4J4bi96jMO3XpQz5aCTzx3xclP+vvMsNnS8ryv9pszl6IpGPnn+ijG88f7kzf8K/550EvvI+1x6ApQAAIABJREFUOT+OxVKrDgH3PUTulO9dhmsEj5tO/sa1ZH/wmvPvOuApcNixb9uMmZ6GpV4cAb364Eg4QN7ieW6NuSQB/v70v/NWho//0Xnt163D2J+m43A4uKdn0XsUps3/jSHvfcb88SOpHe2slD163x30fvIFXnj3E3pcfQU79v7N6B+m8Mi9vfD3d+/yngfHfkds/wdoNfYr/vnsS2z16hL/70HsHzHKZYnkTqt+J2n5CrY95TqBOvrmm3Dk5XFspnt7eMsT4OfLg1d3YMSCFYTarM4XUC5di8Nh0vuyopUhp6/ZytAf5zH3hX7UjgglITGVoT/Oo0fbps6yPyePhZt3M3v9Dl6+tUsVxR9Q+AJNh2kWvtcFYPrqLc74X3ywKP6Jc+nRril1qoeTmZPLwk27mb1uBy/fdm4Pi84vfj/6X38Vw2f+RmhQIPG1ohg7b5mz7O9aVHme9sdahn49hXnvDKZ2ZDXqRUcWW4ls6u9rSUrPoEPTs1/VriIcS2dh6XITPg+/5HxvS2RN5ztcFkx1mWTv+8bXmDs3Yf/2IwCMDlfhc8v9OP6YD0knMeKKhhOaxw+XvFyyGwX4+9H/xq4MnzqP0CAb8QUvoHQ4TO4peK8LwLSlqxg6agLzPnqZ2lER1KsZRb0zXvA8delKktIy6NDc/Qs75MyYjP+tdxP4+ofk/PANllp1sPZ9mJzJ37mW/d/PxL5+LVnvvQKA9eFnMO127Fs3Yaan4VMvjoC7+mJP2E/eb3PdHvf/sXff0VEV7QPHv3M3vVISEgKBJJAQIEgTpIiCilJUwIria0MQG74qVnzt2HsFxIaAYAVEitIEpasUpSX0XkPqpu3O749N22STDSFb8Pd8ztlzuHfv7D65zJ2duVOuOPs463F5DzgKnA/8WG6Yj22qp9YuX59xxCXnYdWajxeu5lRuHm1jo5h017VEhJY9ediqsU2OLJ7GEBYYQGRYMON/XsnxrBxCA/1pGR3BRyOv5sI2LrrjX0FYo0hGfvul3b6S7bFxKZzYsxeTjwmjwh20SUNv49q3XuLmTz9AGQab5ixgxuiH3RKzIyOvH4y2aibOmMmpzCxSElvw6UtPElG/bIUia/EEw5LhMH6+PjSoF8b4ad9zIiMDf19fOrRO4svXnqnUq+Bqeu0voBSqa19UYBAc3of12w9tK82UUAplmOxmo6rYRIx+ZQ8ZVK06QquO6IwTWD95FncZed0gtNZM/HoWp7KySElM4NNxT9iff2vx+S/e9vP1oUF4GOOn/8CJU5n4+/nSoXUiX776lOvPf3YWuQ+PIuC+xwh6/m10dhYF300lf3KFia4mE5QbNmXZvhm/wUPxG3AV+PlhPXqYwuWLKPjqM8jLc23MVRh5w9VobWXiV9/Z8n5SSz599TkiGjg49+XmUJ2TnMSHL4zlrUlfMmfxMhrWC2fUsGsZecM1Lo+5KCODP6+6nlYvj6P9lM8oysxk7/iP2fnqG3bHKZMPysHd++ghgzi5/DcKT9RsyV5XGnFRF1vZv3gNp3LMtI2NZtKd19iX/daSifG28x8W6G8r+xeuLiv7oxry0R2DubC1e8r+0vgv7mqLf9EaTuUU/3ZVjF87ij+E8b9UjH+I2367SuMf2NsW/09LOJWdS0pcUz4ZM9zuaexlk8u9YKGT3GyK3nwM0w13Y7rnGTDnYF34A9Yfp9gfZ9iXPUYb2/OljJ6XYvS0700t+uwN9ErXjnZwZMSVfW3nfvYvnMrKJSUhlk+euJuI8LL5a1517gGys8h5cCSB9z9O8IvvorOzyP9mCvmff2R3mDKZoNycTcu2zfgNGYrf5VejSsr+XxeRP+0TyDO7+68QZwFV3YMSlVKjgPk4vjKu11q/WoPvqNEcF29k6n8HAKOUh1boOEPjtW1ZVL3bOx9W6IyKa4/lTdf2ELiS6cF30TtcOxnelVSLTmRe0sn5gV4obKHtvOv9Z+dTl1VT213fhZFn5xjvS47ZhvdZ5rh+ZSZXMF1+J5afJjo/0EuZBo7EuuIHT4dRa0aPIRSO7Of8QC/kO9HWS2D9w/09xXXB6Gzrzc/o7b7nHtWl8KUbwLufn1hqXkSMl7Q6Het//KBXnkdnM0U/AD4FirTWe8q/gKGuD08IIYQQQoh/F1c+9b4uXt7KWcNlI/AVsEopdW2F97z57xJCCCGEEEL8izhruGit9cfAxcAjSqnPlFJBJe+5NjQhhBBCCCGEsKnRQwW01tuB7sAR4C+l1HkujUoIIYQQQoh/KaW8++WtnK0qVhq61roIeEwpNR/b8LHIKlMJIYQQQgghRB1y1nCptO6r1nqpUqozcKdrQhJCCCGEEEIIe9U2XLTWM6vYnw687JKIhBBCCCGE+Bfz4tFYXq1Gc1yEEEIIIYQQwpOk4SKEEEIIIYTwes7muAghhBBCCCHqkCGDxWpFelyEEEIIIYQQXk8aLkIIIYQQQgivJ0PFhBBCCCGEcCMZKFY70uMihBBCCCGE8HrScBFCCCGEEEJ4PWm4CCGEEEIIIbyezHERQgghhBDCjZRMcqkVpbV29Xe4/AuEEEIIIYTgLJn3vrhRE6+uH1909IBXnkcZKiaEEEIIIYTwem4ZKramcTN3fE2d63poLwB69wYPR1I7Kq49AKNUmGcDqaXxOpOi+wd5Ooxa83lnFt81iPZ0GLV29cnDWFf84OkwasXoMQSA36KaejiS2jn/yH7bP9IPeTaQ2qrfGIC/4+M9HEjtpOzaxfyIGE+HUWv9jh8k86KOng6j1sIW/4Vl8VRPh1ErpouGAbD2LK33dCmu91j//NnDkdSO0elST4dQY17ZnXEWkB4XIYQQQgghhNeThosQQgghhBDC68mqYkIIIYQQQriRksFitSI9LkIIIYQQQgivJw0XIYQQQgghhNeToWJCCCGEEEK4kSEjxWpFelyEEEIIIYQQXk8aLkIIIYQQQgivJ0PFhBBCCCGEcCMZKVY70uMihBBCCCGE8HrScBFCCCGEEEJ4PRkqJoQQQgghhBvJULHakR4XIYQQQgghhNeThosQQgghhBDitCil+imltiml0pRSj1VxzHVKqc1KqX+UUtPO9DtlqJgQQgghhBCixpRSJuADoC+wH1irlJqttd5c7phE4HGgp9Y6XSnV6Ey/12sbLgFJiTR/4TlCzu2EJSOTY9O+4sAbb4PV6jRt/QH9aHzfPQS1aoXVbCZ7wwbSht+J1Wx2Q+T2tNZMmP4D0+f8QnpmJu2SWjL27tto3SKu2nSPvf4BM3/5tdL+uR+/RUKzJi6KtkxkiwT6PjyahG5diElpQ9ryFbzZZ6DTdAFhYVz39su0HzwQwzDYNGcBM0Y/Qs7Jky6P2U5ULMY1I1BxyWDOQa/8Bev86aCryT8mH4yBN6HikiC2JcrPn6L7B7krYjuhrZLo8PI4GnTpTGFGJrunTGPzK69Xm/9bPzqGNo+Ocfje38+NY9vb77kqXIe01kz8aSnTF68iPTuHlPimjB12Ja2bxdT4Mxb++Q/3vfclbeOa8O3T97kw2jKBSYm0ePF5Qjt3pigzgyNTp7P39TdrVPY0HNCfpqPvISg52Vb2rN/AluEjsOZ6qOz5YipffT+L9IwM2rVO5skH76N1UmK16Vp16+1wv6+vL38v/8UFkdrzb9mSxs88Q1CnTlgyM0mfMYOj77xT7fn3bdKEVr/9Vmn/qR9/ZP/o0a4Mt5LgpETavPwC9c49l8LMDPZP+Yq0V9+oNv6WjzxEy0cecvje9udfZOc777sq3EqM5gkE3PcIpjbnoLOzKZz7A/mTJ1QbvxGXQMCoBzESklBh4ej0ExStW0X+Zx+iTx53W+wltNZMnP8bM5b/QXp2LinNY3jiun60jo2uNt17Py5l4fqtHDx5Cq0hPqoht/ftQf9z27ol7pJ6T3C5es/B06z3BBbXe3I8XO+ZOOtnpv/yG+lZOaS0aMbYW66hdVzTGn/GwrUbuO/NSbSNj+XbFx9xYbSeo87+WS5dgTSt9U4ApdR0YBCwudwxI4APtNbpAFrro2f6pV7ZcDGFh5M8Yxrm7amk3noH/nHNafb0k2AYHHjl9WrTRt44lObjnuPQh+PZ99w4fOqFE9azB8rHM3/qxBkz+Wjadzx8x39IiI3h8+9/4rbHnufHCW8Q2aBetWkTYpvw4kN32e1rEh3pynBLxbRNJmXApexatRaTn1+N042Y8TlRrVoy5Y770FYrQ155jlEzp/HGBf1cGG0FgcGY7nkOfXgf1knjIKIxxqDbMJTCOndq1en8/FHd+6L3pMLuraik9u6LuRzf8HB6ff81Wdu2s/KmWwmOi+Oc558Bpdj84itVptv95VSOLFpsty9mQH9a/fc+Di9cXEUq1/n4p6V8NHsRD183gPjGkXy+4Dduf20Ss194gMjwUKfp8wsLeWX6T0SEhbghWhtTeDgp33yFeXsqW265nYC45sQ/+xQYir0vv1Zt2qhhN9DixefZ/8F4dj83Dp/wcMJ79USZPFT2TJ7Gh59N5pF7R5HQvBmfffUNt973EHOmfUZkw4ZVppsx6YNK+0aNeYJO56S4MlwAjLAw4qZMIT81lT0jR+LXrBmNx44Fw+DoG284TX9o3Dhy160r3bakp7sy3Ep8wsPp8v0Msrel8ud/biMovjmtnn0apRSpL71aZbp9X07j2KIldvuiBvQj4f57K+13qZBQgl4bj3XPTnL/9wBGTCwBox4EQ5H/6YdVJlPBoVgPH6Tg5znoE8cwGjfB/+Y7MSW1Jueum8Bqcd/fAHy84HfGz1vOmKsuIT4qgi8WrWL4O18y6393ERledXmSk5fP4O7taREdgckw+PmvLTz0yXcYhuKyTm1cGrMpPJxWxfWetOJ6T+zTT6JqUO+JqFDvMXm43vPxrF/46PsFPDxsEPExUXw+dwm3j3uf2a89QWS9MKfp8wsKeWXKD0TU4HdCuI5SaiQwstyuiVrrieW2mwD7ym3vB86r8DFJxZ/1O2ACntFazz+TuLyy4dLo5pswAgJIHT4Sa3Y2LFuOKSSEJmMe4NAH4237HPBpUJ9mzz7Fnief5tjUr0r3p89b4K7Q7eQXFPDxjJmMvH4INw2yVdw7tEni4pvvYers+fz31qHVpg8M8KdD6yR3hFrJxh/nsWH2XABGfjOZkIiqKzol4rt1pW2/S3j9gn6kLV8BwKkDh3hszRKSL+7N1kVLXRlyKdWzH/j6Yf3kJcg3w7YNWP0DMfrfAIu+t+1zxJyD5fFhts/oNQA81HBJuO1mTIEBrLzldoqysoFl+IaG0vrRh9j+3gfF+yozHzyE+eAhu33JYx4kc/t2Mv7+xw2Rl8kvLOTjuUsZMbA3wy7pAUCHls25ZMwrTF24gv9efZnTz/hk3jKi6oUR26gBqQeOuDpkABrfchOmgAC23DYCS0nZExpKszEPcuD9j2z7HPBpUJ/4555mx9inODKlbAjviXlnVD7XWn5+PhMnT2PkzcO46dqrAOjQri0XDRnKlG9+4IFRd1SZtkOK/d3ljf9sIf1UBpf3vdilMQM0GDYMIyCAvXfdhTU7mxzAFBpKo/vv5/iECVWW/SUKdu7EvH69y+OsSrNb/4MpIIC/bhmOJTubE7+CT2goLR9+iJ3vfVhl/sk/dIj8Q/bXbosx/yV7eypZbrx2/a64FuXvT+7TD0FuDpY/VqOCgvG/5U7yp38BuTkO01n+2YDlnw1l2xv+wHrsCMGvjcdokYg1dau7/gTyC4uYtOB3RlzWk2G9uwLQIaEpfZ98l2lL13D/oIuqTPvYtfblUs82LUg7eJTZqza6vOFSUu9Jq1DviTmNes/xcvWeUx6r9xTy8exfGDGoL8MuuxCADonxXDL6GaYuWMZ/r7/c6Wd8MmcRUfXDiY2KIHXfIafHC9cobqRMrOYQR11GusK2D5AI9AaaAsuVUila61O1jcsrJ+eHX9SbjKW/2l2oJ2fNxhQYSFj3blWma3CF7YI4/vW3rg6xRv7avJ3sXDP9L+heui8oIIA+53Vm2dq/PBiZc1pXzHvOpfTvS8bhI6WNFoDda//g2M5dtO3fty7Dq5bRujN66192DRT913KUnz+qpevvGp+pqEsu4sjipXYNlH3fz8QnKIjIHt2rSWnPt149onpfwL7vZroizGr9lbqHbHM+/bueU7ovyN+PPh2SWb5pu9P0B0+c4pN5v/L4jVe4MsxK6l/Uh/Slv9pVMI/PnIUpKJDwHlWXPRFX2uI8OuMbl8dYE39u+ofsnBz6X9y7dF9QYCB9zu/B8pVrTuuzfvplMUGBAfTp1aOOo6wstHdvspYtsyv7M378ESMwkODzKt7I8z4Rl1zE8SX2+efQ97b806Dn6V27ERdewKHv3Xvt+pzXk6K1K+0aKIVLFqACAvFp3/m0PktnZgCgfHzrNEZn/tqxj+y8fPp1LmuAB/n70btdEsv/2XHan1cvOIhCi+t7jKqr94RWU++pX1zvOeEt9Z7tO8k259G/W8fSfUEB/vTplMLyDZurSWlz8PhJPvlxIY/fco0rw/QKSnn3qwb2A7HltpsCBx0cM0trXai13gVsw9aQqTWvbLgEtmxBXpp9AVNw4CCW3FwCWraoMl1Ip47k7dhJ5A3X0+GP1Zy7dwdtfppFyLmnV+DWlZ37DmAyDJo3aWy3v0WzpuzaV/H/trIde/fTecgttLv8Rm588H+s2ej8ovek6OREjmytXCk9vGU70clu7DmKagJH9tvvSz+Ozs+DqJqPsfWU0MREslLT7PaZDxygKCeXUCfzE8prOuhyDD8/9ru58gOw8/AxW96PirDbnxDTiF2HnA9xfXX6T/Trcg5t41w/n6u8wMSWmFPty5784rInsGXLKtOFduqIOW0HUTcOpctfa+mxfxft5/1IqKfKnt17MZkM4mLt83uLuObs3LO3xp+jtWb+4qVc1Ot8AgMC6jrMSvwTEijYYX/+Cw8exJqbi3+Lqsv+Ek1efZW2aWm0Wr2a6LFjUf7+rgrVoeCWLcmpcO3mFV+7wYlV55+Koq4ciOHnx6EfZtV1iNUyYuOw7ttlt08fPYw2mzGaxTn/AKXAxwcjtjkBI0Zj2fo3lq1/uybYKuw6chyToWjeqIHd/oToCHYdqdl8myKLlczcPH5cs4nft+zgul6uv44Dqqn3BNag3hNxw/W0/2M1nffuoLUn6z0Hj9jK/sb2c7ATmkSx66DznvNXp/xAv24daRsf6/RY4XFrgUSlVLxSyg8YCsyucMxMoA+AUioC29CxnWfypac1VEwp5QukAAfqYoJNVUzh4RRlZlbabzmVgU+98CrT+TaKJKBFAjH/Hc2+F16kKD2dxnePotW0yWzocSFFx907STAzK4egwABMJvv2YVhIMOb8fAoKi/Dzdfxf0KZFPO1bJdKieVNOZmTy2Xc/Mvzx55n6xvOck1zzH0B3Cqpfj9xTGZX256afIiIhzo2BhKDNDoY0mLNRQcGV+jG9jV+9cAozKp/HgoxT+IZXnf8rajpkMOnrN5C944zKiFrJzDET5O+HyaiQ94MCMRcUUlBUhF8V469Xb9nBb39vZ97LjhcacCWf8HCKMiuf+yInZY9fo0gCW7Yg9oH72f38OApPptP03rtoO30Kf3TvReExd5c9WQQFBmIymez2h4eGYM7Lo6CwED9f53fC163fyOGjxxjYt+rhNXXJFB6OxVHZn5GBKazqsfG6oIATkyeTvXw51qwsgrt1I2LUKPyaN2fvyJFVpqtrvlVcu0Wnee02HjKIjA0byXXztatCQ9HZWZX26+xMVIjzuQlBL72HT9eeAFi2bSb38XuhFr33ZyIjN89h2RMeFFBc9ljw8zFVkRo27NzPDa99CoCPYTB2aH8u6ZDs0pihmrx/KgNTLeo9SdMms9ET9Z4cM0EB/pXL/uAgzPkF1Zf9/2znt41bmffm/9wRqjhDWusipdS9wAJs81c+1Vr/o5R6DlintZ5d/N6lSqnNgAV4WGt94ky+t9qGi1JqPPBecSDhwMriL26glBqjtf6quvRnxFFhp1T1haAyMIWEkDbyLjKW2Fbkylr7Bx3WriTq9ls48KrzyZ21pbXGUm7lj5LVIpSD/raSYVjVdcXdPGSA3Xbvrp0YOOIBJkz/ng+e8eIVNhz8/yhn/2+uCcTBPuV4tzdydB6p+XkMiGpEZM/ubHr2hbqOrJJKeV+V5H0Hx5YcU8VqKkUWC+OmzmbUFRfVaAK/SzjMOk7OvWHgExLC1jtGcWrJUgCy1q6jyx+raHz7rex1Mrn2TGitsZQbylJ2/h2VPVT5niNzfl5EeFgo53frcuaB1lQVZX91Ob/o2DEOPf106XbO6tUUHT9OzAsvENC6NXlbttR9nFWpzW9XOf5RjWjQozvbnhtXx4HV0BnEn/feq6jQMIymzfC76Q6CXn6fnPtug8ICFwRaUvaUxVWSrR2VL7rCMVVJbNKIrx+7g8zcPJb9ncq46fMICfBnYBc3DDM+w3pPZnG9J3vtH7T3RL2nurK/pN5TXdn/xbeMGnxZjSbw/xt45ZCn06S1ngvMrbDvqXL/1sCDxa864azHpZfWelTxv28DtmutByulooF5gMOGS/mVCCZMmECH0wyqqrtrprBQijIq35EoUXTKNtcnc8Wq0n3W7GxyNm4i8DSG2NTGmo2bueWRZ0u3u5zThv4XdCcn14zFYrXrdcnKySXQ3x/f01jxI8Dfjwu7dmTJqj/qNO66lJt+ipDIiEr7A+uFO+yJcV0g2RAYXHl/YJDjnhgvU3Aqw+HdWd+wMAod3JFzpOngK0Ep9rthqMnabTu55ZWPS7e7tIqnX9dzyMkrwGK12t15y8o1E+jni28Vdzy/+XUtWbl5DO7ZicziJYQLiyxYrVYyc80E+vlVmbYuFFVR9vjUsOzJWLGydJ8lO5vsjZsISnLtMMk1f67n5nseKN3u2rE9/S7uQ05uLhaLxa7XJTM7m8CAgBqVPUVFRfy8dBmX9r6gRr0zdcGSkYHh4PwboaFYa5j3S2TMm2druKSkuK3hUngqAx8H165PaM2v3ehBV4BSHP6h4ogL19NZWRBS+YaBCg5B51TuianIesA2DNGy9W+KNv1FyNQ5+F7cn8L5rimH1qbu4da3Jpdud0lsTr/ObcjJz69U9mTm5tnKHlP15UeQvx8pzW1LtvdonUCWOZ83fljo8oZLdfUeSzVlj6W47MlyUO8JcHG9Z+2WNG55/t3S7S6tW9KvW0dyzJXPf1aumUD/qsvvbxavICvHzOALupKZkwuUlP2azNI6k+vKfnH2cPbrVf42SV/gGwCt9eHq7thVWIlAr3n69O76mtN2VBrT6RfTGFNwcKUxoOXlpaahrdbK6xwoBVbX3mpvm5jAN++9VLodHBjI0RMnsVit7Dl4mITYsmdX7Nx3gPjYmj/Lorya3in1hMNbUznfwQTe6OQk1s+c475AjhxANWpqf4e2XgTKP7Dy3BcvlJWaSmiF8fCBTWLwCQkma3tqjT6j6VWDOb5qNeYDzudSnam2zZvyzVP3lm4HB/hzJD0Di9XK3iMniG9ctoT3zkPHiG9c9fOndh0+xuH0DHr9t/Ld5vPueZZXRlzPlT06OkhZN8ypaQQlOi57zGlpVaQC83Zb2aMq9gwoVaNnMJyJtsmt+Paz8aXbwUFBHDl2HIvFyp79B0ho3qz0vZ179tptV2fluj85mX6KgZe6fjWxEvk7d1aay+Lb2Hb+83ec5sTqkrvUbuztzUlLI6TCtRsQY7t2K859qUrjIYNIX72GvIOuv3Yrsu7bjalZvN0+FRmFCgzCunf3aX2WPnIInZWBEeO6eWptmzXm68fKVsgL9vfjyKksLFbN3qMniY8uu5G268hx4qMq31hzpk2zaH5YuZ5Ci8Vpo+dM5KXtqDSHt6zsqTrvm6uo9yh31HviY/nmhYdLt4MD/Tly8pSt7D98jPiYqNL3dh48Yrdd0a6DRzl88hS97hpb6b3z7niUV+6+mSt7ubHnV3gtZz1Vp5RSlyulOgI9gfkASikfINBVQWUsXkp47wsxgsvumje48gosZjOZK1dVme7UwkUowyCsR1nl2RQaSvA5KeRudu3E9pCgQNoltSh9JcTG0LFNEiFBgSxYXnYX1pyXz5JVf3BBl9OrfOXlF7Bs7XraJibUdeh15u95vxDeOJoWPctWQGnWuSORLeL5Z57rH1xXwrrlD1RyR/Avy6Kq4/nognx0mnsnitbGkYWLibqoNz4hZfm/6ZBBFOXmcqzcHf2qBMXG0rDLuex302piwYH+pMQ3LX3FN46kY2JzQgL9mb92Y+lx5vwClq7fQq92VfdADLu4O188OsLudX5KEnHREXzx6Ah6tHXt/K70xUuo17s3pnJlT+SgK7HkmslYUXXZc/KXhSjDILynfdkTck47clxd9gQH0a51cukroXkzOrVrS0hwMPPLLUFuzstjyW8r6NW9a40+96efFxHZsAHndTrdPvPay1q6lJALLrAr+8Muv9z2QL3Vq0/rs8L69wfA/Lf7rvnjCxfTsM+FmMpdu9FDbPnn5O/Or93A2KbU63Ku21cTK1G0+nd8zu0OgUGl+3z7XIrOM1O04fR6+43Y5hjh9bEecl0DLDjAn5TmMaWv+OgIOraIJSTAnwV/ll135oJClm7cTq+2zhd4qOivHfuIrh/m0kYLVF/vyapFvSfIDfWe4MAAUlo0K33Fx0TRMSmBkMAA5q8qWznVnF/A0j//plf7qpeUHnbZBXzxv9F2r/PPaU1c40Z88b/R9GjXyqV/iycoL395K2c9LncC7wLRwH+11oeL918M/OSqoI5OnkLU8NtI/GQihz74CP/mzWgy5gEOT/jYbqnAc1YsI2vlKnY9ZJvzkbNhI+nzFxD/5mvsH/cyhSdP0vieUeiiIo58Nrmqr3MZfz8/Rlw/mI+mfUdYSDAJsU34/Ps5WLUufa4LwMxffmXvdf5bAAAgAElEQVTsmx/x8+fv0SQqkqycXEb972WuuLgXzWOiSc/I4osffuLIiZO8NfaBar6x7vgGBtJuwKUA1GsSQ0BYKJ2utj1FftPcnyk0m3kudT2pv/7Gl3fY7rbvWrWGf+Yv5NbJE/huzJOlD6BMXb7Cbc9wAdC/z4cLLscY/hh64ffQMAqj/1D0kll2SySbnhyP3vE31q/KnkqtWncCvwBUE1sDUbW3/RjovamQfswt8e/8bDItRt5Bt8mfsv2d9wmOa06bR8aQ+uEEuyWSL1u3kmMrVvLnaPuho02vGoS1sJD9s93Yy1WBv68vIwb05qMfFxMWHERC40g+X7DclvcvKfuBnfn7Hzz56XcseOVhmkTUp3lURKWVyH747Q/Ss3Pomnz6lY7TdeiLKcTccTvJn33M/vc/JKB5c5o9/CAHJky0W+K286rfyFi5irQHbAsIZG/YyIl580l86zV2v/AShSdP0vSeu9GFRRz89AuXx12Rv78/I2++kQ8/nUx4WGjpAyitViv/KX6uC8DMuQt4Ytwr/PLtNJo0LnuqeEFBAQuX/caQgf0wDPeNxD45dSoNb72VZuPHc2z8ePyaNbM9w+WTT+zK/sQlS8hdvZoDjz0GQKP778cICSF33Tos2dkEd+1KxMiRZMybR/5W9z1DZO/nX9Js5HA6fv4Ju979gMC4ZrR8+CF2fzTBLv/0WvM76StW8fd/H7JLHz1kMNbCQg576Not+PEb/K4aStCzb5A//XPbgyRvGUXBt1PslkgO+XIWRRv+JO912/Bo/1EPgMWCZcsmdHYWRrN4/IfeivXAXgqXuPdZRv6+PtxxWU/Gz11GWFAg8dEN+WLRKqxaM6xPWaN91qoNPPnlbOY/dx9NGtbjwIlTPDl5NgO7pNA0oj65+QUs3LCVuev+4akbBlTzjXXj6OQpNBp+Gy0/mcjh4npPzJgHOFKh3tOuuN6zu7jek1tc74krrvcUnTxJdHG956hH6j2+jLiyLx/9MJ+wkCASYqL4/KfFWK2amy67oPS4mctW8+SEaSx4+2maRDageXQkzSs8YPuHX1eRnpVN1zauHfImzi7VNly01tuBSo8811ovwLZSgEtYMjLYet0NxI17jqQvPqUoM5PDEydx4PW37I5TPiaocBdkxz33E/vUWJo98z+MwECy1q5j6zVDsThY6cUdRl4/GG3VTJwxk1OZWaQktuDTl54kon690mOsxRPcSiav+fn60KBeGOOnfc+JjAz8fX3p0DqJL197hnZJrq+8AYQ1imTkt1/a/y3F22PjUjixZy8mHxNGhfM/aehtXPvWS9z86Qcow2DTnAXMGP0wbmXOwfLBUxjXjMQYMRbMOeils7HOm25/nGGAsq+UGdeOQjUs68423f4oAJap76DXuOfp84UZGSwfci0dXnmRHtMmU5CRSer4CWx+2X6Ct/LxQRmV7wLGXjWYo8uWU3DijBbuOGMjBvbGqjUf/7SEU9m5pMQ15ZMxw+2ehlw2udM7Vk2wZGSw6ZqhtHjpBdpM/hxLZgYHJnzM3tfetDtOmUyoChX6bXePJv7pJ4l/9unismctm6653nNlz803YrVamfDFVE5lZpKS3IrP3n2DiIZly8RarVYslrKyp8SylavJys5x22pipfFkZrJ72DAaP/sszSdNwpKZyYlPP+Xo22/bHad8fOzK/vwdO4gYMYL6112HCgig8OBBjk+cyLEPPnBr/EUZGawdch1tXhlHp6mfU5iZye7xE0mrMEHa8PFBmSo3CBsPGcSJZb9ReOKkmyKuIDuL3DGjCBj9KEHj3kZnZ1Hw7VTyvxhvf5zJx1Z+FrNs24zfkKH4DbwK/PywHj1M4bJFFEz7FPLy3PxHwIjLetrKngW/cSrHTNtmjZl0/01EhIWUHmOtMLE/LCiAyHqhjJ+3nOOZ2YQGBdAyOpKP7rmBC1NcX3G2ZGSw7bobaD7uORKL6z1Hqqj3qAq/uzsd1Hu2ebDeM2JQX9v5n/ULp7JySEmI5ZMn7iGi3KR7byv7xdlDVfegQaXUe9jnKg0cB5ZorX+r4XfoNY1rNqba23Q9ZJtoqHdvcHKkd1Jxtie/j1Jn5wod43UmRfcP8nQYtebzziy+axDt/EAvdfXJw1hX/ODpMGrF6DEEgN/Oguf2OHJ+yVys9LP0qdH1bc+u+js+3smB3ill1y7mR9RuHqI36Hf8IJkXuW4umKuFLf4Ly+Kpng6jVkwXDQNg7Vla7+lSXO+x/vmzhyOpHaPTpeDdI51KrWnczKtbbV0P7fXK8+hsqNg6B/saAK8ppWZord928L4QQgghhBBC1ClnQ8UcDs4ufr7LCkAaLkIIIYQQQgiXq9WsS6212flRQgghhBBCCFE3av4ExGLFSyH/B/D+B2IIIYQQQgjhZbxyAslZoNqGi1IqC9uE/PLnNxf4FdtSyUIIIYQQQgjhcs7muIRW974QQgghhBBCuIPToWLFQ8P6A8nFuzYDC7TWRa4MTAghhBBCiH8jGSpWO9VOzldKxQD/AA8BMUAT4BHgn+L3hBBCCCGEEMLlnPW4vAh8VPF5LUqp0cBLwC2uCkwIIYQQQgghSjhruHTTWt9acafW+l2l1DbXhCSEEEIIIcS/l1IyWKw2nD3HpbrnteTWZSBCCCGEEEIIURVnPS7hSqmrHOxXQJgL4hFCCCGEEEKISpw1XH4FrqjivWV1HIsQQgghhBD/eoaMFKsVZ89xuc1dgQghhBBCCCFEVaptuCilbq7mba21/rKO4xFCCCGEEEKISpwNFeviYJ/CNnysCSANFyGEEEIIIU6DkrFiteJsqNh9Jf9WtnXbhgGPAquAca4NTQghhBBCCCFslNa6+gOU8gFuBR4CVgMvaa1P5xku1X+BEEIIIYQQdeOs6MpYHxvn1fXjDvt2e+V5dDbH5R7gfmAR0E9rvcctUQkhhBBCCPEvJc+frB1nc1zeA44C5wM/lnvKp8I2Of+cmnxJ0eM31DpAT/J56SsALG+O9nAktWN68F0Aiu4f5OFIasfnnVmMUmfv44LG60wK7xrg6TBqzfejuRSNucbTYdSKz+vfAlB07+UejqR2fN6fA4Dlk6c8HEntmIY/B0DRY0M9HEnt+Lw8Hcszt3o6jFozPfM5lg8e9nQYtWa657Wz9vybnvkcAMu4EZ4NpJZMYz8GoOCOyzwcSe34TVrg6RCEizlruMS7JQohhBBCCCGEqIazyfkyNEwIIYQQQgjhcc7muGTheHJ9yVCxs3ccjxBCCCGEEB4gc1xqx1mPS6i7AhFCCCGEEEKIqhieDkAIIYQQQgghnHE2OV8IIYQQQghRh5SMFasV6XERQgghhBBCeD1puAghhBBCCCG8ngwVE0IIIYQQwo1kpFjtSI+LEEIIIYQQwutJw0UIIYQQQgjh9WSomBBCCCGEEG4kq4rVjvS4CCGEEEIIIbyeNFyEEEIIIYQQXk+GigkhhBBCCOFGMlKsdry34dKoCcYVt6KaJUJeLnrtYqyLvgOtq05jMmFcej0qNhGaJqB8/Sh6/Ab3xVwF1bUvqv35EBgMh/diXfIdHDtQfaJmrVAp3VCN41DhDbGunIdeOc89AQNExWJcMwIVlwzmHPTKX7DOnw7aWnUakw/GwJtQcUkQ2xLl50/R/YPcFXGpyBYJ9H14NAnduhCT0oa05St4s89Ap+kCwsK47u2XaT94IIZhsGnOAmaMfoSckyfdEHUF0bGYrr8LlZAMuTlYf1+A9adpzs//oJtR8cmoZokoP38K7xrgvphLRDXFGDwc1TzJlnfWLML68zfOY+9/A6pZEsQmoHz9KRpzjftiLi86FuPaUaj4Vrb4V/yMde5XzuO/4j+ouGRoVpz3773cbSE7orVm4qotzFifRrq5gJToBjxxcUdaR9WvNt2K3Yf5fuMu1h88zsHMXO7u0ZZ7z09xU9TYyv4rbysu+3PQa5dgXfhtDcr+obY0JWX/Y0PdF3N5kTEY/W+C2Ba2364/l6GXzqw+fgD/QFS/G1HJnUAp9PYN6HlTwJzjnrgr0FozcV0qMzbttuWfqHo8cWE7WkeGV5tuxd6jfL95H+sPneRglpm7uyZxb7dk9wT9Lzn3AKrHAFTnCyEwBA7txvrzdDiyz3nCpPYYFw6GBlGQfgy9/Ef0lnUujxeAxs3wufFuVEJrMOdgXT4fy+wp1ZadKi4Jo88VGIkpEN4A0o9hWb0E67yvoajQPXGLs4p3DhULCMY0fCygsX75BtZF36N6DcS45Nrq0/n6o7r0QRfmo/dsd0uozqgufVHdLkOvXYh15kQozMe45h4ICq0+XVxrVEQMet92dGG+e4ItERiM6Z7nQIN10jisC2ag+gzC6O+kEejnj+reF11QgN691T2xOhDTNpmUAZdyZHsaR7an1TjdiBmfk9T7fKbccR9f3HoXzbt0YtTMaS6MtApBIfjc/yKgsXz0PNa5X2FcchXGFTdVn87PH6PnZVCQj965xS2hVhIYjGnkU6A11s9ewbrwW9SFV2Bcdl316Xz9UF0vtl27uz147QYGY7rvBVv8E1/AOm866qIhGANvrD6dnz+qx2W2+Hd5Lu+X9/HqLYxfuZnh57Xmg6vOJ8jPh+FfL+VYtrnadMt3HmLbsVN0ax5FoK/JTdEWCwzGdMdY2/mf/HpZ2d+3BmV/Vy8o+wOCMG5+GNBYv3oX/etsVPd+qD5DnCY1rr0bFZeMdfZnWGdOQjWJxxg62uUhV+XjdWmMX7Od4Z0T+eCK8wjy9WH4Dys5lpNXbbrle46y7Xgm3WIjCfRxY/75F5171aM/6vyB6JXzsX79PhTkY9z4AASHVZ+waUuMq+9C79mGdfo76LRNqCEjIL6N64MOCsH3oZdBQ9EHz2L5cSrGpVdjGvSfapMZXS5ERTbGMu9rit79H5YlP2LqezU+Ix5zfczirOSVPS7qvEvA1xfrlLcg3wxswhoQiHHx1bDsx+J9DuTlYnluhO0zul8KLd14l9ARkw+q6yXoNQvR65cDYD24G+OOZ1AdLkCv+KnKpHrZLPSymQCoFu3cEW0p1bMf+Pph/eQl27netgGrf6Ct4bLo+6rPvzkHy+PDbJ/RawAktXdj1GU2/jiPDbPnAjDym8mERDR0mia+W1fa9ruE1y/oR9ryFQCcOnCIx9YsIfni3mxdtNSVIdsxeg0APz8sE16APDN661+2H+XLb7T1XORVff6LHrre9hkXXg7JHdwWcwnV/VJb3vniNVs+Sd1oyzuXXgdLZlV/7T51q+0zevaDRPfm+RKq1wDw9cc6aVzxeV6PNSAIY8ANsPC7as+95RHbHX51weXQyjN5v0R+kYVJq7Yy4rzWDOuUCECHmAj6TpjDtL/SuL9X1ef34T4dePQi2xiGxWlOeobrmK3s98M65U1bXknbZMs/l1wDvzop+5+9w/YZ3S/zWNmvzu0DPn5YZ7wH+Xnonf+AfwCq92D073Mhv4pKf9MWqJbtsHz2IhQ3vKxZ6ZhGPA0JbWDnZjf+FcX5549URpybyLD28QB0aFyfvp8tZNrGXdzfvXWVaR8+vy2P9irOPzsPuyVe+Pece0w+qO790CvmodctscVzYAfGPS+jzr0I/evMKpMa518Oe1PRP08HQO/ZhoqMweh1OdZdrv07jAsHgq8fRR8+Z+vtAggMwnTFTVjmfwN5uQ7TWeZ9DdkZpdt620YoLMDn5v9Cg0Zw8qhL4xZnH6/scTFatUdv32j3I6U3rED5+aPiqy4wvU5MPMo/EL3tz7J9RQXonX+jnN4BcdK17UJG6862ynL58//Xctv593RjsAa0s2EBDqT070vG4SOljRaA3Wv/4NjOXbTt37cuw3NKtT0XvflPu0qydd2vKL8AlIcq9DVlJHdEb9tgn3fW/27LOy3ccNfvDBltOqO32J97/ccy27lv6d3nvry/Dhwnu6CQfsmxpfuC/Hzo3TKG5TsPVZvW8ODAa6NVh6rL/gTvL/tV4jnoHX/bVZL136tRvv7QvOrhUirxHHR2RmnFGYADu9DpR1Etz3FlyA79degk2QVF9EuMKd0X5OtD74Qolu+uviLpqfzzbzn3NG2JCgiyH95VWIBO24BqUc3vr8kH4lpVGhamN6+FJi3AP9BFAdsY7bpg/ecPuwaKdc1SlH8AqlU1ZWe5RksJvXcHACqsXp3H6U0Mpbz65a28suFCZAwcO2i/L+MEuiDP9t5ZQjWIQlstcOqY/RsnD9vuJHirqCZwZL/9vvTj6Pw8iGrqmZhcLDo5kSNbKw8xObxlO9HJSW6NRUU3RR+ueP6PofPzUNGxjhN5i0ZNKs/fOnXcdu02auKZmE5HVFMHef/YWZf3d53MxKQUzeuH2O1PaBDGrpOZHoqqBs72sj+iMRyv0DDMOIkuyEdFND69dADHDlWfzkV2pWfb8k+9Cvmnfii70rPdHk+N/EvOvYqIttUbTh6xf+P4IWgYXXXC+pEokw+6wt+ijx9CGYZtzosLqehYOFxhDs7J2v1uqRZt0FZL5d9BIfDWhktgMNpRt6I5BxUY7P54ass/CAryK08MzDPb7gIZbh4/XlNBIWhHkxLN2aigs+j8n4ag+vXIPVX5zk9u+imC6rv5rk9QCJgdVA5ys23vebPAYMd5JzcHFejlsYMt7+c6PvfK2899ORl5hQT5+WAy7Iv48ABfzIUWCiwWD0XmRFX5x3yW5J+AIMdDYvJyIDCoymSqinTaSTpXycgvJMjXhMmwv+sa7u+LuchCgaWahSo85V9y7gmoqt6Qi/Krpt4QUBxrxeGUeTn277tKUAg618G1m5OFcjKn105YfUwDb8C6clGVw8vE/2/VNlyUUiOUUonF/1ZKqc+UUplKqY1KqU4ujczhcB+FJ4dQOaWMcq9qutlK3/Liv8VhbMq7Qz5TDvKcUsr5ijQuicXBPoVnYjldjmI8W2KvivLeskdrTZHVWvqyWG2VSkclUMlfoBy+6808dB3WRlW/Xc7Cr226M1Q5/9i+0NFTvcvyj5c6y8697Wsc1BuqrP/UQKW0Jenc8Mc4LPtPo+w0+eAzaizkm7HMmFCnoXkjpbz75a2cTc6/H/i8+N83AOcA8UBH4B2gl6NESqmRwEiACRMmcPvpRmXOsS0dXFFAENrspS3wpi0xXVe2Conel4re/hf4+dtyQPkL2j/QtlKY1QvvWoHtzr6j8x8Y5Phu6L9AbvopQiIjKu0PrBfusCfGtcFkg6OerYBgjy7PWSPVXbt5Xh47VJ/3Hd1N9AJr9x3j1ulLSre7xEbSLzmWnIIiLFarXa9LZl4hgb4mfE3e2dmOOcfxneGzJf/k5VYRf2C1d491Xq7Du9JV9QbUpbUHTnDr92Vz+7o0aUi/xJji/KPtel0y8wsJ9PHS/HMWnnuaJWH6z8NlsezZZpuj4h9Qud4QEIguyAdrFb2lJbFWPAcl21UtLFJXqvrdCgx23IvtgGn4w6iY5hS+/KDt84RwwFnDpUhrXbKQ9uXAZK31CWChUurVqhJprScCE0s2ix5fUtWhjh07iIqMsW+jhzdA+QdUHv/sLY7swzL1tbLtgnwICccwTFAvEtLLTWhsEOXdK2UcOYBq1NT+/NeLQPkHVh7//y9xeGsq5/fqUWl/dHIS62fOcWss+vB+VMX5FPUjUAGB6IpjiL3N0QOoRk0qXLsNbXnnqHtXqKqVI7Zzfzbl/bZR9fn6P2ULSAT7+XAk24xFa/amZxPfsGwJ1V0nM4lv4GRJVU9yWPY39O6yv7zjh2xzJsoLa4DyC8DqaB5F+XSdHMyli2iM3vpn5f11qG2jenx9/QWl23b5JyOH+HLzpHalZ9tte5Wz8NxzeA+WT18o287Pg9D6tnpD/Ub281waNoYT1azSln4MbSlCNYxG7y2br6kaRqOt1spzZuqYPryv8lyW+pE1/t0yXT8Ko0N3it58vPJcGSHKcXbbxKqUaqyUCgAuBhaWe89lS1RYt21AJZ4DfgGl+9Q53dEF+ehdHno+hTOF+baHQ5W80o/CwV3ofDMqqdyytD6+qIQUtIuXJjwT1i1/oJI72q1Cojqebzv/aX97MDLX+XveL4Q3jqZFz26l+5p17khki3j+mfeLW2PR/6xDtelsd/6NzhegC/LQqZvcGsvpsm79C9Wqve2OYTHVoact7+zw3jxfwrr5D1TrTvZ5v3Mv27lP885zH+zvS0rjBqWv+IZhdGwSQYifLwu2lVUAzIVFLN1xkF4J7p9wXFPWbetRSe0dl/2eejbRadCpG20rL5aPP6WrrYd9T9XP99GpG1Gh9aBZYtnOmDhUg0botI2uDJlgPx9SouqVvuLrh9CxcQNC/HxYkFrWWDQXFrF012F6xXnnwjJn47mnIB8O7Sl7nTwC+9NsvUCtzy07zsevbNW0qliKYPc2VOvOdrtVmy5wYEfVS4nXEeumtRgp59r/bnW5EJ2fh95Wfdlp9L8e4+IrKZr0KjrtH5fG6U2UUl798lbOGi5PAeuA3cBsrfU/AEqpC4GdrgpKr14IRYUYNz2IapGC6nIRxsXXoH+ba3fxmca8hXHVSLu0Kqk9KqUrqnFz23ZKV1RKV6hXeRiQy1mK0GsWorpeimrfC2KTMC6/3fZk3vXLymJu3QXjv29BaLknWofWh8QOtpfhAw2ibf+Oc/2SoPr3+bbzP/wx2/nsfilG/6HoCs/hMD05HuOGe+3SqtadUO17oJok2Lbb90C17wH1I10edwnfwEA6XT2ITlcPol6TGEIiI0q3fQNthepzqev5z6T3S9PsWrWGf+Yv5NbJE+gw5AraDxrI7VMnkbp8hVuf4QJgXT4Xigox3fkkKrkD6vx+GAOHYV0006673+fZSZhuut8urWp7LqpjT1RsC9t2x56ojj3dtoqdXvkzFBVh3PIwKrEd6rxLMC69Fl3h+Uumx97DuPYu+9iTO6LO6YaKibNtn9MNdU43qO++a1cXn3tjxBOoVu1RPS/DGHAjerH9uTc9PRHjRvsH1Kk2nVEdeqKa2p57oTr0RHXo6da8X8Lfx8Qd3ZKZuGoL0/5MZeWeIzwwawVWrUuf6wIw6+9dtHvtaw5klA3DOpCRw4Jt+1iwbR+FFis7TmSyYNs+ljlZRrkulJb9/3kQ1TIF1fVijEuuQf/2U4Wy/22Mq++0S6uSOqBSzkPFlJT956FSznNr2a/XLbHl/+vvhYQ2qM4X2p4jsnKB3TK9xuhXUFeWG0S9fwc6bRPGkBHQujMkd8K46k70nm3uf44IxfmncyIT16UybcMuVu47xgPz1mHVlD7XBWDWln20e+9HDmSWDak6kJnLgtSDLEg9aMs/J7NZkHqQZbtdfMf/X3LusRShV85H9eyP6twb4pJteV0p9LpFpYepdt0xHh8PYQ1K91l/mwPNW6H6Xg/NklAXXQ0tU7Aud/2oAeuvP9mev3LPU6jWHTEu6I/pypuw/vK93ZA73xc/w3TLA6XbRtc++Fx9u20y/qnjqITk0hch4S6PW5x9qh0qprWeo5RqDoRqrdPLvbUWGOqyqPJysHwyDuPK2zBueRjMOejf52Jd+K39cYYJKqyaYwwejipXUTANs10glm8+Qv+5DHfTa38BpVBd+6ICg+DwPqzffgi5WWUHKYUyTHazoVRsIka/sielq1YdoVVHdMYJrJ8869qgzTlYPngK45qRGCPG2s7/0tlY5023P84onlBYfte1o1ANy5ZdNN3+KACWqe+g1yx2bdzFwhpFMvLbL+32lWyPjUvhxJ69mHxMGCb71VkmDb2Na996iZs//QBlGGyas4AZox/G7XKzKXr7cUxD78Z019NgzsG6eCbWOVPtjzNVzv+mG+6xO/8+I8cCUPTFm+hVC3E5cw6WCc9iDBmOcftjYM5FL/sJ689f2x/n6Nq9agSqXAPLdPMYACzT30evW+rqyG3MOVjeG4tx7SiMO5+y5f3Fs7DOnWZ/nKP4r7/bPu/f8TgAli/fQq9ehLuNOK81Vg0fr9rCqbwC2kbXZ9J1vYkILrsjbdVgqTChds3eo4ydt6Z0u6QRExMWxMJRV7g2aHMOlkkvYAy6DeOWR2zn/7e5WBd+Y3+cg7xvDKlQ9t9Uruz/41fXxl0iLxfr5FcxBtyEccN/bQ/iW/kzeukP9sc5yD/Wbz9CXXYDxqDhtkrq9vXoeRWueTcacW5LrGg+Xpdqyz9R9Zg0uDsRQeXzj66cf/YfZ+zC9aXbC9IOsiDtIDGhgSy8zYXPxPoXnXu9Yp6tXtBjgG0l1UN7sE57C3KqrzewPw3rd+MxLhyM6nShbSn6mZPAHSM8crMpfOMxfG68B5/7noXcbKy/fI9l9hT74wzD7vyrtrZ1nkw9L8XU81K7Q4s+fR3rCveOeBDeT53Ow/qUre+oD3AjcIXWuiYLg+uix2+oZXie5fPSVwBY3hzt5EjvZHrwXQCK7h/k4Uhqx+edWYxSXjwe34nxOpPCuwZ4Ooxa8/1oLkVjrvF0GLXi87rtJkfRvZd7OJLa8XnfdofU8slTHo6kdkzDnwOg6DHX3d9yJZ+Xp2N55lZPh1Frpmc+x/KBB2661BHTPa+dteff9MznAFjGjfBsILVkGvsxAAV3XObhSGrHb9IC8OKF78pLTW7p1UslJm5N88rzWKOlQZRS5yml3gH2ALOB5UDVj6IVQgghhBBCiDrk7Dku45RSqcCLwCZsyyAf01p/UWHomBBCCCGEEEK4jLPlkEcC24CPgDla6zyllFd3bQkhhBBCCOHNvHnlLm/mbKhYNDAOuBJIU0p9CQQqpZw1eIQQQgghhBCizjhbVcwCzAPmFT/L5XIgCDiglFqktb7RDTEKIYQQQggh/p+rcc+J1joP+Bb4VikVClzlsqiEEEIIIYQQopxqGy5KqQfdFYgQQgghhBD/H8gUl9pxNscltNxrTIXtENeGJoQQQgghhBA2zua4lD6iXSk1uPy2EEIIIYQQQrjL6awOJssgC/Z04jIAACAASURBVCGEEEIIcYZkOeTacTZUTAghhBBCCCE8ztnk/E2U9bS0VEptLP++1vocVwUmhBBCCCGEECWcDRW7CogC9lXY3xw46JKIhBBCCCGE+BeTkWK142yo2FtAptZ6T/kXkFv8nhBCCCGEEEK4nLOGS5zWemPFnVrrdUCcSyISQgghhBBCiAqcDRULqOa9wLoMRAghhBBCiP8PDBkrVivOelzWKqVGVNyplBoO/OGakIQQQgghhBDCntK66sezKKWigB+AAsoaKucCfsAQrfXhGnyHPP9FCCGEEEK4w1nRlbGnXSuvrh8337TNK89jtUPFtNZHgB5KqT5ASvHun7TWi0/nS5Y0alLL8Dyrz9EDAOgdf3o4ktpRLToB8F2DaA9HUjtXnzxM4V0DPB1Grfl+NJdRKszTYdTaeJ2JZc4ET4dRK6bL7wRgdsPGHo6kdq48ccj2j9wMzwZSW0HhAKyPjfNsHLXUYd9ulkc19XQYtdbryH6K7h/k6TBqzeedWVh+/97TYdSKqedVwNmd9wEsS6d7NpBaMvUe6ukQakxGitWOszkuAGitlwBLXByLEEIIIYQQQjjkbI6LEEIIIYQQQnhcjXpchBBCCCGEEHVDyVixWpEeFyGEEEIIIYTXk4aLEEIIIYQQwutJw0UIIYQQQgjh9WSOixBCCCGEEG4kU1xqR3pchBBCCCGEEF5PGi5CCCGEEEIIrydDxYQQQgghhHAjGSpWO9LjIoQQQgghhPB60nARQgghhBBCnBalVD+l1DalVJpS6rFqjrtGKaWVUuee6XfKUDEhhBBCCCHcSBln91gxpZQJ+ADoC+wH1iqlZmutN1c4LhQYDayui++VHhchhBBCCCHE6egKpGmtd2qtC4DpwCAHxz0PvArk1cWXSsNFCCGEEEIIcTqaAPvKbe8v3ldKKdURiNVaz6mrL5WhYkIIIYQQQriRt68qppQaCYwst2ui1npi+UMcJNPl0hvAW8CtdRmX1zZcgpISSXrxBcLO7UxRZgaHpn7FrtfeBKvVadqIgf1pPvpegpNbYTXnkbl+PX/fPgJrrtkNkdvTWjPh61lM/+kX0jOzaJfYgrGjbqF1i7hq0z325kfMXLis0v65E14nIbaJgxR1K7RVEh1eHkeDLp0pzMhk95RpbH7l9WrPf+tHx9Dm0TEO3/v7uXFse/s9V4VbWXQspuvvQiUkQ24O1t8XYP1pGuhq8o/JB2PQzaj4ZFSzRJSfP4V3DXBfzMUiWyTQ9+HRJHTrQkxKG9KWr+DNPgOdpgsIC+O6t1+m/eCBGIbBpjkLmDH6EXJOnnRD1JVprZm4aA0zVmwgPcdMSrNonhjch9ZNGlWb7r35K1i4KZWD6VloNPGRDbi997n079jKLXGHtEqi3csvUP/ccynKzGDPl9PY9uob1eb9Vo88RKsq8v7m518kzZ15v5jWmgmffs5X33xH+qkM2rVpzZOPjqF1q6Rq07Xq2NXhfl9fX/5e87srQrXjn9iSps89S3DnTlgyMznx1XQOv/VOteffr2lT2qz8rdL+9Nk/suee+1wZbiVBSYm0ePF5QjvbfruOTJ3Ontdr9tvVcEB/YkffQ1ByMlazmaz1G9gy3M2/XVGxGNeMQMUlgzkHvfIXrPOnOy87B96EikuC2JYoP3+K7nc0YsQ9tNZM/GkpM5asJj07h5T4pjxx4xW0bhZT489Y+Oc/jH5/Cm2bN+Gbp+91YbRlzva8X0JrzcR5y5mxbC3p2bmkNG/CE0P70zq2cbXp3pu9mIV/beHgyQy01sRHRXD7pT3p3yXFTZGL8oobKROrOWQ/EFtuuylwsNx2KJACLFW2Vlo0MFspdaXWel1t4/LKhotPeDgdvp1OzvZUNt1yG4FxcbR85ilQBrtefrXatI2H3UDiS//H3n3HN1X1Dxz/3KRNmnQCXXTQwSoUZAmyl4sloAgKqKBQxIkDNz99RPER9/M4mAooIEOZshQB2TJkzxbKKhu6m7Zpcn9/BNqGpoPSpOV5fd+vV19wb+5Jvr09Ofeee9aHnP52Asfe/xA3P1+qtW+Hoq2cX3XyvMVM+HkBrw0bTHRYCNMXLufJd8ax9LtPCajuV2La6PAQPnp5pN2+0KAAZ4YLgLuvLx0WzCP9yFG2PDYUz8hI7vjgX6AoHPxofLHpTvw0iwt/rrHbF9KjO/VfeoHzq9cUk8oJjF64jfoI9fwpLBM+QAmoiabfcNBosC75sfh0Oj2advejnjiKevwQSkxT18VcSEhsDI163Efi1u1odboyp4ubO52g+nWYOfwFVKuVB8ePZeSi2XzesZsToy3elDXbmfjHVkY/0JGowOrM+Gsnwyb+wuLXhhDg41lsusycXPq2jKV2UA20GoXf98bz6sxlaDQK9zcp+ab7Vrn7+tJ2wVzSjxxl++NDMUZGEjv2PRSNhsMl5P2TM2dzcc1au33BPbpRd9QLXHRl3i9k8g8z+G7KD7z+0gtER0YybeZsho58jt/m/0yAv3+x6ebO+L7IvpGjXqV50zucGS4AWl8f6vw8i+yj8RwfFoc+IoKQ/3sHNBrOf/p5qemTPviQzO0787fzkl1baXfz9aXR/J/JOhrPwSFP4REZQfT774JG4eTHn5aYNmjwQOp89AFnvp1I4thxuPn64tfBxdcugyfa58ainj+Ndeo48K+Jps+TaBQF6/JZxafT6VHa3It6Mh5OHEap18R1MTswZflfTFy6htEDuhMVHMiM3zcw7LPvWfzBSwT4epeaPsds5pM5y6jh4+WCaG1u97xf2JSVG5i4/C9G97uPqGB/ZqzewrAvf2Txe8+WeP4zs3Po27YptWsG2sr+nQd5dep8W9nfItaFv4Eoo+1AXUVRooAk4FFg0PUXVVVNBfIvNoqirANG30qlBapoxSVkyONoPDzYP3Q4lowMkv/agJu3F5GjX+XUN99hychwmM69ejXqfPAv4t/+P87NnJ2///Lyla4K3U5Obi5T5i9hxIA+PPbA/QA0bVCXu4e+yKylq3hpyCMlpjfo9TSNqeuKUO1EP/kEWoMHW4Y8RV56BrAed29vGrzxKke//vbavqJMZ89hOnvObl/M6FdIO3qU1P0HXBC5jaZDD9DpsEz6ELJNqId3gYcRTa9BWH+fD9nFPL00ZZL3qu1vounUCyqp4rJ36Qr2LFkOwIj5P+LlX6PUNFGtWxHb7R4+69iNhA2bAUhJOseb29YSc3dnDv+5zpkhF5FjzmPqmm3E3d2Kwe2bAdA0IoR7x01l9qbdjOrerti0b/bpbLfdrn4kCeevsGTHQadXXCKefAKNhwfbhwwrlPe9qPf6aBJKyPvZZ8+RfUPer/fqy6QfjSfNhXn/upycHCZPn8GIJ4fw2KMDAGjapDFde/Rh5tz5vPzcM8WmbXpHY7vtvfsPkJySQq9u9zs1ZoAajz2GovcgccRIrBkZZGzYiNbLi+BXXuLihElYiyn7r8s5dpysXbucHmdxgoc8hsbDg0NPxtmuU+s34ObtTa3Rr3DmmwnFXrvcqlcjeux7HHvnXc4XunZdWeHaa5fSrhu467B+/2/IMcGRPVj1BjTdB8KfC2z7HDFlYnlrsO09OvSASqy45JjNTF2+jrienRl8d1sAmtapxb2vfcLsP7cw6qH7Sn2PH1asJ7CaL7UCqxN/5oKzQwZu/7yfH4fZzNSVG4nr1oHBXe4CoGl0OPe+/SWz125jVN+7i0375oDudtvtGtYh4dxFlmzd8z9ZcdFU9b5ipVBVNU9RlOeBVYAW+EFV1QOKoowFdqiqusQZn1slB+fXuLsLV9f+ZVfIX1i4GK3RgF/bNsWmC+jTG4Dzc+c7Pcay2HXwKBlZJrp3aJ2/z+jhQZe7mrN+555KjKxkQfd05cKadXY3aacXLMLNaCSghPN/I3c/P4I6d+T0r4ucEWaxlNg7UQ/+Y1dBse74C0XngVK3cQkpqwZVVUs/6AaNut9L6vkL+ZUWgBPbd3LpeCKx3e+tyPDKZNeJs2Rk59KtSUH3LqPenc4No9lwOPGm38/P6IHZYqnIEB0Kursrl27I+0kLFuNmNFDjJvN+QOeOJP260BlhluqfPXvJyMik+3335O8zGgx06dSBDZu23NR7LVv5uy1txw4VHWYRPl06kb5+vd1NWvKSpWgMBrxa3+X0z79V1bt2IWWd/bXr0iLbtcu3beti0wX0fgCAC5V87dI0aGF70FOogqLu2oCi06PUuT266+xKOEWGKYduLQvKeqNeR+emMWzYd6TU9GevpPD9ivW8PaiXM8Ms4nbP+9ftOnaajOwcut1ZUNEw6nV0vqM+Gw7E3/T7+XkaMec5v+wX5aOq6nJVVeupqlpbVdVx1/a966jSoqpq51ttbYEqWnEx1qlDVkKC3b6cpLNYMrMw1q1dbDqf5s3ISjhGzcEDabN7B52STtBixVJ8Wt7yejflcvzMWbQaDREh9v06a4eHknj6bDGpChw7nUSLfk/RuPfjDBr9L7btO1hqmorgXbcu6fH259+UlEReZhbe9creAhTWpxcanY4zC1xccQkOQz1/xn5n8iXUnGyU4HDHiW5zwTF1uXD4aJH95w8dJTjGua0UjiRevIpWoxARYN8dMjqoOokXy9aFIc9iJc2UzdKdh9h09CQD2jj/Ka5X3TpkFJP3verWKfP7hPS25f2khYsrOsQyOX7iJFqtlsha9vm9dlQkxxNPlPl9VFVl5eo/6dq5IwaDR8UG6YC+dm1yEo7Z7TOfPYslKwt97eLL/utqff4pTU4cI3bHNkLeHYPioXdWqA4Z6tYhK94+/pwkW/yGOsXnH+/mzTAlHCN40KO02rWddmcSabJiKd53tnB2yPaCQuHCjWXnZdScbAgKc20s5ZR47qLtuhtk3x0yumYgieculZr+k7nL6NbyDhpGOH8saWG3e96/LvH8Zdv5D7TvKRAd7E/i+ctleo88i4W0LBNL/97LpoPHGNCpcu7hRNVUYlcxRVH6qar6q4P9OuANVVU/cEpQfr7kpaYV2W9OTcXdt/hxIfrAAIx1ahP58oscGzsO89Vkaj3/LE3mzGRr6/aYL5XtS1NR0jIyMRo80Grt64c+Xp6YcnLINeehc3f8J2gYHUmT+nWoXSuUq6npTFuwjGHvfMSsT//FHfXLfgNVHjo/X8ypqUX256am4O7rW+b3CXuwL8m795Bx7HhFhlc6oxeYHDSrZ2XYXvsfZKzmR1ZK0b9ZVnIK/tGRLo8n1ZSDUadDq7HP+74GD0y5eeTmWdC5aYtNv+fkWQb+dw4AbhoN7zzUlXsaOzffA7j7+WJ2WPak4O5X8pi0wkIe6kPK7r1kujrvX5OWlobRYECrtT/Hvt7emLKzyTWb0bm7l/o+O/7ZxfkLF+l5f+ndayqCm68vlrSi59+SmoqbX/FljzU3h0vTZ5C+fgPW9Ay82rQm8NmR6CMiSBwW58yQ7bj5+pKXVvR7mJdScvy6wAAMdWoT/vIoEj8YR97VZMKef4ZGc2ayo00H1127jF6opsyi+00ZKEZPbr4t2PVSM7Mx6h2UPZ4GTLlmcvPy0Lk5vu7+fegYm/bHs/zfr7oiVDu3e96/LjXLVO7zD7Dn+GkGjp8KXCv7B/bgnqYNnBqzuL2UNsZlhKIow4FnVVVNBFAUpTu26c2c2/nWQXcZRSmlG41Gg5uXFweGPc3VtesASN2+gzb//E3YU0+SOL7kwZG3QlVVLIVm/lCuzRKnOOjDqF4r/kvq3vhEX/u+np1bNqPnyNFMmruYb991QaHq6PyjONzviEdQIAHt2rDv/Q8rOrKycRSmQpnjvy05/M6U/W9W/o9VsVgLPuN6vnaUv8uS9wHqBgcw76VBpJlyWH8okXEL1uCl19GzeUxFhV0sh2XMTZxHfVAg/m3bcNBFeV9VVSyFutFdL3Mclz3XjnE4i2VRv638HV8fH9qX0M2pohV3/ksq+/MuXiLp/97L387YuhXz5cuEf/QhhoYNMR10TWs1UEzZU0r+uXbtOjx8JMnXrl1p23fQcudWQp4aysnxnzklVMeKKTyrYNFZ5Lqbn/cdHwvF5/08i4WPZi9l5ANdyjSA3xlut7xfrvNfSuFfNzSIeW+NIM2Uzfp9Rxn383K8PDzo2arqd/O+Wbf5EJdKU2LFRVXV+xVFGQisVhRlNrZpzQKAR1RVLXaQRuG5nydNmsTNDi/PS0nFzdenyH6tj4/Dp1nXmZNTAEjZXNCH25KRQfrefRjrO3eQ+7Z9hxjyZkEDVMvGDejeoTWZWSYsFqtdq0t6RhYGvR73Ep463MhDr6PTnU1Zu+2fCo3bkdyUVIctK+4+PpgdPBFyJKxvb1AUzlRGV5msDDA6mLXKwxMcPU38H5CVnIJXQNGZogx+vg5bYirS9mNnGDqhoG9+y9phdGtSj8ycXCxWq92TtzRTDgadG+7a4ltbwDYeplF4MABt60WQbsrh82UbnF5xMaek4u6g7HH39nHYCulISB9b3k9a6JRxiUVs2/kPT8QVDLZv1aI53e67h8ysLCwWi12rS1p6OgYPD9yLaektLC8vj9//XMt9d3cpU+tMRchLTUXr46Ds9/bG4qAlrCQpy5bbbt4ax7qs4pKXmoqbg/jdfLwd9iLIT5fi+NqVsXcfxnou7OqZlQEGB2Wnwei4JaaSbT+SyNBPpuRvt6wfRbeWjcnMdlD2ZGVj0LnjXkxL7y/rt5OelU2fts1Juzb9tDnPgkW1kpZlwqDTFZu2ItyOeX/70RMM/WJ6/nbLepF0axFb8vkvtezX0SjS1k2vbYPatrJ/wR//kxUXUT5luXOeB8QCLwMpQFdVVYt2pi/khrmf1bVj3r+poLISEjDe0B9YHxKCm6dnkf7Dduni41Gt1iLVWAUFrM59XBRbJ4r5XxU8YfU0Grh4+SoWq5WT584THVYwf/zxM2eJCi/7fPKFlfVJ6a1Ij4/H+4b+/IbQENy8PEk/WrbBdWEP9eXy1r8xJZU+lqeiqefPoNzYH7uaP4qHAfX8aceJbnPnD8fTvkPbIvuDY+qxe1GFLVjrUGxYEPNeyp8BEU+9jgupGVisKqcupxAVWD3/tcSLV+22y6phWCALtx/AbLGUeuG7FRnxCUXGsniE2PL+jWNfihP6UB+ubt1G9lnX5P3YBjH8MnN6/ranpycXLl7EYrFw8vQZoiMj8l87nniC6KjIMr3vlm3buZqcTM9urukmBpBz7Bgedez787vXrInW05OcY8WX/Q5df0rtwpYCU3wChhvGYepCbPGbEorPP1lHE1CtVhRFsQtXURTbNc1VLiShBIbZnzI/fxS9oejYlyogNiKUef/3XP62p4eeCylpWKxWTl24QlTNguUDEs9dstu+UeK5S5xPTqXjyx8Vea3182P5OG4Avds0q9hfoJDbMe/HRoQw762C9Qntzv/Fq0QFFzxMS7xw2W67rBrWqsnCzbucXvaL20eJg/MVRWkP7AJqYFtk5nlgqaIoYxVFcdrIryt/rqV6l05oPQue/AT2fQBLlsnuiVSRdL+vRtFo8GtXcAOn9fbGq0ljMg4494mbl9FA43q183+iw0Jo1rAeXkYDqzZszT/OlJ3D2r//oWOLmxtonJ2Ty/qde4itG1XRoRdxYfUagrp2xs2r4PyHPdiHvKwsLpVw/q8zhodTo+WdnHHxbGLXqQd2oDRsAXpD/j5Ni46oudmo8fsqJSZn27/iD3xrBlO7XUGXnlotmhFQO4oDK/5w6md7euhoFB6c/xMVWJ1mkSF4eehYtafgGYcp18y6g8fpEHPzeXhX4lmCfb2cfuG68OcaArt2Rlso74c+2Ju8LBNXypD3DeFhVG95J0kLXDebmJenJ41jG+b/REdG0LzJHXh5ebLyj9X5x5lM2axdv5EO7co2O9qylb8T4F+Du1w4QDxt7V94d+qIplDZ79e7F1aTiYytf9/Ue/n1tC0em7XXdd/5q2vWUq1zZ7trV0Cf3liyTKRu3lp8uj9s1y7fG69ddzQm04Xd3KyHdqLENLMrO5Vm7VFzc1AT9rssjrLyNOhpFBWW/xNVM4BmdWrhZdCzakfB392Uk8u6PYfo0Lj4RWwH3d2G6a/H2f20b1SXyCB/pr8eR9uGzh1jdzvmfU8PPY0iQ/N/ooL9aVY7HC8PPat2FkwDb8rNZd3eo3SIvfmeL7uOnSK4ms//ZKVFUZQq/VNVldbi8hUwXFXVbde2FymK8gfwLrAHcEq/jbMzfiIs7ikaTZ/Kqa+/wyOiFpGvvcrpiZPtppm86++NpGzeypGXbStWp+/Zy6UVK4n56jOOf/hvzFeuUuv5Z1DNeST9MN0ZoZZIr9MR1783E+YsxMfLi+jwEKYvXIZVtfJY74I1ERb9uZ53vpzE799/RWhQAOmZWYz81yc80KU9ESHBJKemM2PRci5cvsqXb77o9LiPT/uR2iOG0/rHHzj6n2/wjIyg4eujif9ukt00sffv2MKlzVv458VX7NKHPdQHq9nMmSXOfdJfHOuG5Wi69Eb79Bjbui3+wWh6Dsb65yK7KZLd3p+KGr8Py8z/5O9TYu+0LaYWbnvypTSzrTeinoyHqxddEr+7wUDjHran3H6hIXj4eNO8n20V6n3Lf8dsMjE2fjfxf23kp+G2FZ0Tt27jwMrVDP1xEr+OHpO/AGX8hs0uX8MFQO/uxvCurZj4x1Z8DB62BSjX78RqVRncvmB9nMU7DjJm7ipWvjWM0Oo+JF1NY8zcVfRsFkNYDV+ycsys3p/A8t1HeLdf8fP/V5ST034kOm4YrWZ8T/x/v8UzIoL6r4/m+AT7vH/39s1c3ryFPaPsx5uFPtQXq9nM2SXLnB5rSfR6PSOGDuG7qd/j6+NDdGQE02b+jFW18vi1dV0AFi1dxtvvf8gfSxYQWmj2w9zcXFav/YsHe/dCo3Hd5JNXZs4k4MmhRE2eyIUJE9HXqkXwyy9xccpUu2liG2xYR8bWvzn92hsABL/8EhovTzK378CSkYHXXa0IHPk0KctXkH34sMviPz9jJqHDn6LBtCmc+eY7PCIiiHjtFZIm2V+77ty6kdQtW4m/du3K2LOXyytWUu/LT0n88N+Yr14l/LlnUc15nPthhsviVzethI690Ax7E3X1AqgRhKb7o6hrF9tNkawdMxH12H6sP3+Tv09p0Bx0Hiih0bbtJrZKmHoqHpJLn82roujd3RneozMTl67Bx2ggqmYAM37fiFVVGXx3QaV98aZ/GDPtV1Z+PJpQ/2pEBPkXmYls0aadJKdn0Som2ulx3+55/zq9uzvDu7Vn4rL1+Bg9iAoOYMbqzbayv2vBtM6Lt+xmzI+LWfnhKEJr+JF0JYUxMxbRs1VjwvyrkZWTy+pdh1i+fT/vunhqalG1lVZxaaWqql07taqqmcAbiqJMd1ZQeamp7O73CPX+PY7GP00jLy2NMxOnkHjD6rGK1g3lhlr4oWdfoPZ7/0ed999DY/AgdfsOdvcbQF4Z+6dXtBED+qCqKpPnLSYlPZ1GdaP5Ydzb+FcrmKHIarUNcLveqqtzd6O6rw8T5yzkSkoaep07TRvU5adP3qVxvdKnRbxV5tRUNjzYn6bjP6Lt7B/JTU0jfuIkDn5sP0BUcXND0RR9ChL+UF8urt9A7pUrTo/VoawM8r56C+2jz6J95j0wZWJdswjrbzes/KzVwg03ZdqBz6HUCMrfdhvxDgB5M75A3boaV/AJDGDELz/Z7bu+/U5kI66cPIXWTYvmhrw/9dEn6f/lv3nih29RNBr2/baKuS++5pKYHYnr2hKrqjJlzTZSMk3Ehgcz9emH8fcueKJoy/sq1/s0+Bj0BPh4MnH131xOz8TboKdOUA0mDO9LpwbOv3kwp6ay+aEBNB4/jrtmzcCcmsaxiZM5Mt5B3nfwBDD0wT5cXr+x8vJ+ISOeGoJVtTLphxmkpKbSqGEM0yZ8g3+NgmlKrdcG9qs39ClZv2kz6RkZ9LzftWsAWVLTSBg4iLAPxhI97XssqWlcmvo957/4yu64G8v+7GPHCHw6jhqPPoLi4YH57FkuTpzEha+/dWn8eamp7Hv4UWr/+0Ma/jgdS1oqSZOmcPLTL26IX4tyQ9lz5NkXiX5vDNHvv4fGYCBt+3b2PvyIa69dpkws376L5uERaOLeAVMm6rolWFfMsT9OowHFPn5N/5F2Zaf2KduNtWXWf1C3rXF66IXF9eiE1aoyZfk6UjKyiI0MY+qrw/AvNOjeesPA8sp2u+f9wuK6dbCV/Ss3kpKZRWxECFNfegJ/n4JZPfPP/7VubT5GDwJ8vZm4fD2XUzPwNnpQp2YAE54fTKfGrp/SX1RdSmmL3SmKEgg8h22ciwocBL5VVbWsj5/VtYGunQ+9onS5mASAesz5A+KdQandHIBfqwdXciTl0+/qeczP9KjsMMrNfcJyRipFB1veLiaqaVh+m1TZYZSLttfTACypUbOUI6um3lfO2f6TVTkPXG6Z0Ta5x+7wyMqNo5yanj7Bhttk3RJHOlw4Q96oPpUdRrm5/Wcxlk0LKjuMctG2ewi4vfM+gGXdnJIPrKK0nR8FXDAYuAJcbh1bBefqK+C/9UCVPI+ljXFpB2y/tvkjMPPa/7dde00IIYQQQgghnK60rmKfA31VVd1VaN9iRVEWApOAuxwnE0IIIYQQQoiKU1rFxeeGSgsAqqruVhSlclZoEkIIIYQQ4jZWlWfuqspKmy5GURSlmoOd1cuQVgghhBBCCCEqRGmVjy+B3xVF6aQoive1n87AimuvCSGEEEIIIYTTldhVTFXVyYqinAU+wDarGMAB4ENVVZc6OzghhBBCCCH+10hPsfIpbYwLqqr+BlTOSoJCCCGEEEIIQSkVF0VR3i3hZVVV1Q8qOB4hhBBCCCGEKKK0FpdMB/s8gWFADWxdyIQQQgghhBBlJLOKlU9pY1w+v/7/a9MfjwKeBOZgW+NFCCGEEEIIIZyu1DEu16Y+ojfY7gAAIABJREFUfgUYDMwAmquqmuzswIQQQgghhBDiutLGuHwKPARMBhqrqprhkqiEEEIIIYQQopDSWlxeBXKAMcA7hfrjKdgG5/s4MTYhhBBCCCH+5yiyjHu5lDbGRU6rEEIIIYQQotJJxUQIIYQQQghR5ZU6OF8IIYQQQghRcWQ65PKRFhchhBBCCCFElaeoqursz3D6BwghhBBCCIFtAqkqL7XjHVX6/th3/d4qeR5d0lXM8v27rviYCqcdNhaAtHuaV3Ik5eOz+h8ArJsXVnIk5aNp+yB5ox+u7DDKze2zX7D8Nqmywyg3ba+nGancnhMHTlTTALDMHl/JkZSPdtAbAJif6VHJkZSP+4TlAFi+GV3JkZSP9vnPsMz/orLDKDdt/1ewrJtT2WGUm7bzo+S92LuywygXt/8uAUC9cLySIykfJSgagF+rB1dyJOXT7+r5yg6h7DRVsl5Q5UlXMSGEEEIIIUSVJxUXIYQQQgghRJUns4oJIYQQQgjhSjKrWLlIi4sQQgghhBCiypOKixBCCCGEEKLKk65iQgghhBBCuJAsQFk+0uIihBBCCCGEqPKk4iKEEEIIIYSo8qTiIoQQQgghhKjyZIyLEEIIIYQQrqSRMS7lIS0uQgghhBBCiCpPKi5CCCGEEEKIKk+6igkhhBBCCOFKMh1yuUiLixBCCCGEEKLKk4qLEEIIIYQQosqTrmJCCCGEEEK4kCKzipVLla+4qKrK5K2HmLs7gWRTLo2Cq/P23c1oEFStxHSbT5xnwd5Edp+9zNm0LJ5tG8vz7Ru5KGrQ1IrC4/k30DZsjJqRgXnFQnJ+mgxWa/FpIqLxGPkKmqi6KD6+qClXyNuxlZzpE1CvXnZZ7IWpqsrkZeuYs2YryRmZNIoK453BvWlQK6TM77H6nwO88PVPxEaG8st7Lzgx2kKCwtD0HYYSUQ9Mmajb/sT6+3xQiz//aN3QdB+IUqsehEejuOvJG/2wa+IthqqqTP5zG3M37yE500SjWsG83bcLDUIDS0z39crNrN4Xz9nkdFRUogKq81TnO+nerL7TYw6oHc29r71IdOuWhDRqSMKGzXzRpWep6Tx8fBjw1cc06dsTjUbDvt9WMffF18m8etXpMTuiqiqTN+5l7o7DJGdl0yjEn7e7t6ZBcI1i01isVqZt3s+6+NMcu5QCQGzNGozq2oLGoQGuCTw4HO0jz6BEx0BWJtZNq7Aum1163u/zBEpUDEqtuig6PeZnergm3mKoqsrkHQnM3X/CVvYH+fF2x0Y0CPAtMd3mU5dYcPAUu88nczbdxLOt6vH8Xc7P9zdSVZXJf+1i7raDtvwTGsDbvdrRoKZ/sWksVivTNu5l3ZGTHLuYDEBsSACj7m1J47CSv/MVTVVVJq/YwNz120nOyKJRRChvP9qdBuE1S0z39ZI1rN51iLNXU1FVlaggf566rx3dW7rg+hscjqbfCJSoGDBloG75A+uKOaXn/V6PoUTWh/A6KDo9eS/2dn6sJVBVlUkz5zJn0TKSU9NoHFOPd0aNpEHd2qWmTU5N48vJ01mzaSvpGZmEBAfy9GOP0LfbPU6P27t+PZp+PI7qLVtgTk3jxMzZHBz/WYn3PQ3eGE3DN0Y7fG3/2HEc+eprZ4UrblNVvqvYlL8PMXHLQYbd1YBvH2qPUefGsHnruJRhKjHdhuPnOHIphdYRQRjctS6K9hovb4yfTARUst59hZyZk9E9/Dj6ISNLTKZ4emE9n0T25C/Jeus5cmZMwq35XRg/+i9oXPw7XDNl2TomLPmT4T068d2oIRj1ep76dCqXUtPLlD7HbGb8nGX4+3g5OdJCDJ5oR7wLqop12nisq39B6fQAmvsHlJzOXYfS6m5Ucw7qiaOuibUUU9ZsZ+IfWxnWtSXfDuuLUefOsIm/cCkts8R0mTm59G0Zy+eP9+Q/Qx6gYVggr85cxqo9zv+9QmJjaNTjPi4cTeDC0YQyp4ubO516ndszc/gLzBj6DBEtmzNy0WwnRlqyKRv3MnH9boa1a8y3A++xnfsfV3IpI6vYNNl5FqZu2kvjEH8+frAj4x/siJtWw2PTlnHgrAsePhi9cBv1EaBimfAB1uU/o7nnITQPPFZyOp0eTbv7ITcH9fgh58dZBlN2JjBx+1GGNa/Dt71aYXR3Y9iiLVzKzC4x3YaTFzlyJY3W4f4Y3Cqn3ASYsn43E9f9w7COTfn2sW4Y9e4Mm7aMS+kl5B+zhanrd9E4NICPH+7K+P5dbflnymIOJF1yYfQwZeUGJi7/i2H3t+fb5wZh9NAx7MsfSy37M7Nz6Nu2KZ/H9ec/Ix+hYa2avDp1Pqt2HnBuwAZPtM+NBVSsU8ZhXTkXpUtfND0GlpxOp0dpcx9qbg5q4mHnxlhGk2fNY8KMnxk+qD8TPv4XRoMHT77yNpeulPwQJyMzk8dfeI3DCccZM+oZJn0ylsce6o05L8/pMbv7+tJhwTxQVbY8NpRDn35B3WdH0vDN10pMd+KnWay9r4fdz/XKyvnVa5wet7j9VOkWl5w8C1O3HiburgYMbl4XgKYh/tw76Tdm70pgVIfGxaZ9rUtT3uhqa4Zbk5Dkkniv0z3wMIpeT9a/RkNWJpZ//kYxeqF/YgQ5c2dAluObTsvBvVgO7i3Y3rMT66ULeH4yAU10XawJri1Uc8xmpixfR1zPzgy+py0ATetEcM/o8cxavZmX+t1f6nt8v2I9QX4+hAdWJz7pgrNDBkBpcx+467DO+BRyTBC/F6vegOa+AbB2sW2fI9lZWN4danuPdt2gbvH5yxVyzHlMXbONuLtbMbh9MwCaRoRw77ipzN60m1Hd2xWb9s0+ne2229WPJOH8FZbsOMj9Teo5M2z2Ll3BniXLARgx/0e8/ItvobguqnUrYrvdw2cdu5GwYTMAKUnneHPbWmLu7szhP9c5M+QicvLymLppL3Ht72Bwq4YANA0L5N7/zGf2tkOM6trCYToPNy2rXuyPr0Gfv691dAg9vv6VWdsP8VGfDk6NW9OhB+h0WCZ9CNkm1MO7wMOIptcgW4tjdjF535RJ3quP2N6jUy+IaerUOEuTk2dh6s4E4u6sy+AmUQA0rVmNe6f/yey9JxjVJqbYtK+1b8gbSiwAa46fd0m8N8ox5zF1/W7iOjZjcGtbS0PTWkHc+9lsZm/dz6h7WzlM5+GuZdWrg27IP6H0+GoOs7bu56N+XVwUv5mpKzcS160Dg7vcZYs/Opx73/6S2Wu3Marv3cWmfXNAd7vtdg3rkHDuIku27uH+FrFOi1lp3x3c9Vi//7ctnx8Bq4cRTfeB8OeCEvO+5c1Btvfo0BPqN3FajGWRk5PLlFnzGPHYAB7rZ2v5aRrbgLsHDGHWgqW8FDek2LSTfppLrtnML1P+i4felodaN3fN7xP95BNoDR5sGfIUeekZwHrcvb1p8MarHP3622v7ijKdPYfp7Dm7fTGjXyHt6FFS9zu5slvZZFaxcqnSLS67ki6TkWumW0x4/j6jzo3OdULYcPxcCSlBU4kZwq1lO/J2bLGroJjXrULxMOB2h+MbnuKoaakAKO7uFRpjWeyKP0mGKYfure7I32fU6+jSNIYN+0p/cn/2Sgrfr/iLtwY94Mwwi9DENEM9sseugqLu3oSi06PUbujSWG7FrhNnycjOpVuTgm4uRr07nRtGs+Fw4k2/n5/RA7PFUpEhOqSq6k2nadT9XlLPX8ivtACc2L6TS8cTie1+b0WGVya7Tl8kI8dMt9io/H1GnTud64WzIeFMsem0Go3dTSeATqulTqAfVzNLbiWuCErsnagH/7G7SbPu+AtF54FSyRXxm7HrXDIZuXl0q1PQLcno7kbnqCA2nLxYYtrKLPuv23XqAhk5uXRrHJ2/z6hzp3NMBBviTxebzmH+cdNSJ7A6V0tpaapIu46dJiM7h253FlQ0jHodne+oz4YD8Tf9fn6eRsx5zi17NA1aoB6yz/vqP+tt5X4d13UTv1W79h8kIzOL7l065u8zGjzo0u4u1v+9o8S0C1b8Qb+e9+dXWlwp6J6uXFizzq6CcnrBItyMRgLatinz+7j7+RHUuSOnf13kjDDF/4AqXXFJvJqGVlGIqGbfzSi6ug+JV9MqKarSacIjsZ4+YbdPvXge1WRCUyuy9DdQFHBzQxMWgcfwF7Ac3o/l8H6nxFqS4+cvodVoiAiy75MdHRJI4rmSbx4APpmzjG4t7yA2MtRZIToWGAqXbmhlS7mMmptte+02kXjxKlqNQkSAn93+6KDqJF4s27iPPIuVNFM2S3ceYtPRkwxoU7lPE4sTHFOXC4eLVobPHzpKcIxzW4gcSbycait7qvvY7Y/29yPxcupNvVdunoUDZ69Q29+v9INvkRIchnr+hopV8iXUnGyU4HDHiaqgxOQM2/n3u7Hs9yIx2fGT26ok8XKK7btbw348TnSAH4nXxj6VlS3/XKJ2oPPzz3WJ5y/byv5A+9bS6GB/Es+XrctjnsVCWpaJpX/vZdPBYwzodKczQi0QFAoXbyj3ky+j5mRDYJhzP7sCHT91Bq1WQ0SY/TjS2hG1SDxVfKX3zNnzXElOwcfLkxGv/R+Nuz5Amwce4d/fTCbXbHZ22HjXrUt6vH3XYFNSEnmZWXjXq1vm9wnr0wuNTseZBVJxEY5V6a5iqdlmjDo3tBr7+pWvhzsms4VciwWdtvL6MBdH8fZGzSjaD1jNSEPx8nGQwp7xo69xa2nrmmU5cpCsd16AcjzFvlVpmSaMel2R8+9jNGDKNZObl4fOzXEW+vvQMTbuP8qKjx0PunMqgyeqyUF3vKxMFIMXrj+T5ZNqysGoK3r+fQ0emHLzyM2zoCuhD/+ek2cZ+N85ALhpNLzzUFfuaVzHqTGXl7GaH1kpRSsEWckp+EdHujwe27l3d3DudZjMeTdV9kzasIe07Bwebu6CAeJGLzA5uLHPyrC9dptIzcnF6K5Fe8OsO756d0x5FnItVnTaqvvcrfj8o7fln1K+u4VNWvcPaaYcHm7RwBmhOpSa5bjs9/UsvewH2HP8NAPHTwWulT0De3BPUyfHb/RyXO6bMlCMt0+5n5aejtFgQHtD+eLj7YUpO4dcsxmdgx4Yl67aJnP4dMIP9Ly7E1M+/YDDxxL5cvJ03LRaXntmmFPj1vn5Yk4tWobnpqbg7lvyhBqFhT3Yl+Tde8g4drwiw6uSZFax8imx4qIoyn9Lel1V1ReLSTcCGAEwadIkhpWhfFZVFUuhm3Plhn/tjs1/rQr/0R1VNBSlTBWQ7G/Go3j7ogmthW7wMIwffUPmqCfBnOuEQG1UVcVSaOYP5Vp3C0e9Lko7/3kWC+NmLWHkA10J8PWu6FDLxuH5L2Z/FWA7/4Xyv2L/r92x1/4CpfWIqRscwLyXBpFmymH9oUTGLViDl15Hz+bFjw+oVA7+NkoZvzO39rHFlD2Ozr16/ZiylT1/HT3NpA17eP2+VkT5l/3ifUscna6qnvftzv/1sqfoOS44/1VHsd9dh8faH1Oav46cZNJfu3i9e2uiApzT4nJTZb+q2h1TnLqhQcx7awRppmzW7zvKuJ+X4+XhQc9WTu6u6DCPO78MKS9VVbFYCp/7a/86yD2lnXv12sxpdaNq8cHrowBo3aIpmVlZTJo5l+efHIzBw6Miw3cURJFdyk2cf4+gQALatWHf+x9WdGTif0hpLS4jgf3APOAsZbxeqKo6GZh8fdPy/bulptl++hJD56zN324ZHkC3mHAyc/OwWK12T37Sss0Y3LW4V9Enbmp6OngVvWFXPL1QM0ufjcuadBo4jeXwfvL27cJr5lLc7+6OeeViJ0Rrs/3IcYaMn5K/3bJ+FN1a3UFmdm6R85+eZcKgc8e9mCeG8//aTnpWNn3bNScty9bf2JxnwWq1kpZlwqDTFZu2QpgyweBZdL+HETW75Nm4Ksv2Y2cYOmF+/nbL2mF0a1KPzJyi5z/NlINB54Z7KU/8jXp3GoUHA9C2XgTpphw+X7ahSlZcspJT8AooOk2swc/XYUtMRdp+8jxDZ6zI324ZEUy32Cgyc8wOyp5cDO5uZSp79iVd4pVf1jKgRX2eaO28Qcl2sjLA6Cjve9q+F1XQ9qQrDF24JX+7ZWgNutUJuVb2q3atLmm5ZgxuVavs3554jqE/LM3fbhlZk26Na5OZ6yj/5FzLP6WXf/vOXOSVuasZ0LIBT7S9o9Tjy2v70RMM/WJ6/nbLepF0axHrsOxPy8q2lf2llj06Gl3rIty2QW1b2bPgD+dWXLIyHJf7BqPjlpgqYNvufQwZ9Ub+dsumjenepSOZpiwsFotdq0t6RiYGDz3uxbR0+Xrb7jlaNbPvDty6eRO+/mEmp5LOUb92lKOkFSI3JdVhy4q7jw/mtLJ17Q/r2xsUhTMLnXevI25/pVVcagL9gUeAPGAu8KuqqskVHUhsUDXmPV4wCNdT58aFDBMWVeVUcgZRNQq6WCVeTSOqeuldriqL9fQJtOGRdvuUgCAUgxHrqRM39V7qxXOo6aloajp3bEZsRBjz330+f9vTQ8+F5FQsViunLlwhqmbBGhTHz10iqmbxawoknr/E+eRUOrw0rshrdz33PuPjHqF322YV+wsUdjEJJTDU/sGzbw0UvaFoH+gqIjYsiHkvDcrf9tTruJCagcWqcupyClGB1fNfS7x41W67rBqGBbJw+wHMFkuZbpxc6fzheNp3aFtkf3BMPXYv+s2pnx1b0595cQXrNnjq3LmQnmkre66m27WUJF5OKVPLyYkrqTwz+w9aR4fwTvfWTonbEfX8GZSgG/rzV/NH8TCgni++f3xlig30Y96AgtnWbGV/tu38p2YSVWiMY2Jyht12VRAb6s+8Zx7K3/bUuXMhLdP23b2SZtdSkngppUwtJycup/DMTytoHR3KO72Knz2wIsRGhDDvrRH5254eei6kpNnK/otXiQoueKCQeOGy3XZZNaxVk4Wbdzm37LmQhBIUZl/u+/lfK/eLn1CjMsXWr8P8yf/J3/Y0Grl46TIWi5WTSeeIrlXwXT5+8jRRtYofpxYeWhN396K3dNcbOzQa51b20+Pj8a5r3xXZEBqCm5cn6UfLNqFD2EN9ubz1b0xJZ50RovgfUWLFRVXVK8BEYKKiKKHAQOCAoihvqKr6U0UG4ql3p1FN+5uxEF9PvHTurDpympFtbU8sTeY81h07S/8mpS/EVFnytm9C3/8JMBjBZJuz373zfajZJvL27ryp99KERaDxrYb1nHNvuD0NehpF2d/whPj74WXQs3L7Xp7pbZv+0pSTy7rdh+jfyfF0ngCD727DPc3tZ++asuwvzly+yvtDHiS6hEpPRbAe3oWmc2/Qe0CObSYepWk72zz9xw469bPLy9NDl986cl1INR+8PHSs2nOUkffabn5NuWbWHTxO/9Y3/wR2V+JZgn29qlylBWD/ij/o+e4b1G7XmmObtgJQq0UzAmpHcWDFH079bE+9O41C7G/GQvw88dK7s+pgIiM72qYGNpnzWHf0NP1blDxW5VJ6FnEzVxFe3ZvP+nUuMk7AmdQDO9Dc2w/0hvxZ9TQtOqLmZqPG73NZHDfDU+dGoyD7m/kQbwNeOjdWJZxlZEvb5Awmcx7rEi/QPzaiMsIslqdeR6MbFhcN8fPCS69j1f7jjOzSHLj23T1ykv53ljzW41J6JnHTlxNe3YfPBtzt9Pzj6aHPbx25LqSGL14eelbtPMDInp0AMOXmsm7vUfq3v7mZMQF2HTtFcDUfp5Y91kM70XR90C7vK83b28r9BNdPblMWXkYjjW+YfCQ0KBAvTyOr1m7gmSG2NWhM2dms3fw3Ax7o7uhtANC5u9P2zub8/c8eu/1bdu7G4KEnIrTkhUNv1YXVa6j3wrO4eXmSl2Fr4Qp7sA95WVlc2ryllNRgDA+nRss72fXqG6Ue+z+jCsyAeDsq0+B8RVGaY6u03AusAG7u7ruc9G5ahreOYeLmg/h46Iiq4cOM7Uewqmr+ui4Ai/cnMmbFdlaO6Emor62pOCk1k/3nbTMvmS1Wjl1JY9WR0xjc3egY7dwvcO7SX9D1fRTjvz4jZ+50NDXD0D/xNLm/zrKbItlrxmLy9u4k+/Oxtt93xEtgtWA5tB81Ix1NRBT6AUOwJp3GvG6VU2N2RO/uTlyPzkxYugYfTyPRNQOYvmoDVlXlsXsKno4v2rSTMT/8yqrxrxHqX42IIP8iM5Et3LiT5IxMWsU4v8Kpbvkd2vdAM+Q11LWLoHoQmvv6o65fajdFsvbNr1GPHcQ6f0L+PiWmmW1BspBI2/YdtgqDejoBkl2wgGAhenc3hndtxcQ/tuJj8CAqsDoz1u/EalUZ3L5gnY3FOw4yZu4qVr41jNDqPiRdTWPM3FX0bBZDWA1fsnLMrN6fwPLdR3i3X/HrL1QUd4OBxj3uA8AvNAQPH2+a9+sDwL7lv2M2mRgbv5v4vzby03BbK1/i1m0cWLmaoT9O4tfRY1CtVh4cP5b4DZtdvoYLgN7NjeHt7mDi+t22ssffjxlb99vKnlYFlfLFe+IZs3gjK1/sT6ifF9nmPEbM+p00Uy5jurfh6IWC2d/ctVoa1ix9TZtbYd2wHE2X3mifHmNbt8U/GE3PwVj/XGQ3Tazb+1NR4/dhmVnwtFeJvdOW98Nt31Glme1Jv3oyHq6WPotgRdK7aRneog4Ttx/FR+9OVDUvZuw6bjv/TQq6uyw+dJoxf+5h5RNdCfUxApCUlsX+i7aZu8wWlWNX01mVcBaDm5aOkUGuid/djeEdmzJx3T/4GHREBfgxY9M+rCoMblMwNe/iXUcZs3AdK18eSGg1b1v+mbGCtOwcxjzQrmj+Cbn51o7yxe/O8G7tmbhsPT5GD6KCA5ixerOt7Ol6V0H8W3Yz5sfFrPxwFKE1/Ei6ksKYGYvo2aoxYf7VyMrJZfWuQyzfvp93B/VyaszqxhXQsReaYW+hrv7Vlve7D7RdAwrlfe3/TUJN2I/154IV2ZUGzUHvgRJmy1tKU9v1TT0ZD8muXfhTr9cRN3gAE2b8jI+3F9ERYUyfu9B23e1X0DK8aOVq3hn/Jb///AOhwbZ8/dyQQQx+fjRv/fsLet7diSPHEpkyex7PPjEQnU7n1LiPT/uR2iOG0/rHHzj6n2/wjIyg4eujif9ukt0Uyffv2MKlzVv458VX7NKHPdQHq9nMmSXObWEXt7/SBue/D/QCDgFzgLdUVXX+EqyFxN3VAKsKU7YeIiU7l9jgakwd0Bl/z4JBZlYVu8GdANtOXeSdFdvyt1cdOc2qI6cJ8TGyeqST1xXJSCfrtZF4vPAmxg++Qs1IJ/fXWeT8OMn+OK0WCj1Nsxw9iK7vo+h6PAQ6HdaL5zFv+JPcn6dBtuvm8C8srmdnrKrKlGVrScnIolFkGN+PHoZ/oUH3BYM7q8gASFMmlknvo3lwGJqn3gRTFur6ZVh/n2d/nMb+/ANoHopDqV7QIqR9wjYrmmXON6g71jk78iLiura0nf8120jJNBEbHszUpx/G37ugL7fVen1wsO38+xj0BPh4MnH131xOz8TboKdOUA0mDO9LpwbRxXxSxfEJDGDEL/YNste334lsxJWTp9C6adHc8PR16qNP0v/Lf/PED9+iaDTs+20Vc18sedVlZ4prf4ft3G/cS4oph9gQf6Y+3g1/L0P+MQVlj+3cX8k0ceTazeYzP9u3FIX4erH6pQHODTorg7yv3kL76LNon3kPTJlY1yzC+tss++O0RfO+duBzKDUKbuzdRrwDQN6ML1C3rnZu3A7EtahjO/87Emxlf6AfU/u2wd9YsEaFFQdlf9IV3lm9O397VcI5ViWcI8TbwOqhrqm4AMR1bGqLf/1uUrKyiQ0NYOrQnvh7GfOPsd4wsP9Khokj568A8MxPK+3eL8TPi9WjB7smeCCuWwdb/Cs3kpKZRWxECFNfegJ/n4KuetbrZf+1v4GP0YMAX28mLl/P5dQMvI0e1KkZwITnB9OpsZOnNTdlYvn2/9A8/DSaEWPAlIm6dgnWFT/bH6fRFC33Bzxjl/e1T70JgGXmV6jbXL96+4jBA1CtVibPmktKajqNYuryw+fj8K9eLf8Y67WB/YWz/x0N6/Pdx//iy0nT+G31Wmr4+THy8UcZ8dgjTo/ZnJrKhgf703T8R7Sd/SO5qWnET5zEwY8/sztOcXND0RRteQt/qC8X128g98oVp8cqbm9KSYvFKYpiBY4D1x9XFEwoBaqqqmXpr1KmwflVkXaYrSUk7Z7mlRxJ+fis/gcA6+aFlRxJ+WjaPkje6IcrO4xyc/vsFyy/TSr9wCpK2+tpRipVdyxZSSaqtsGgltnjKzmS8tEOsnWXMD/To5IjKR/3CcsBsHxTCdOhVwDt859hmf9FZYdRbtr+r2BZN6eywyg3bedHyXuxd+kHVkFu/10CgHrh9pzOVwmyPdz6tXpwKUdWTf2unoeqNfFgsTIeaF1FnvY65rV0a5U8j6V1FXPeFBRCCCGEEEIIUUalDc4/6Wi/oiha4FHA4etCCCGEEEIIUZFKG+PiAzwHhAJLgD+A54HRwG5gVvGphRBCCCGEEDcqbSFX4VhpXcV+ApKBLcBw4DVAB/RRVXV3SQmFEEIIIYQQoqKUVnGJVlW1MYCiKFOBy0AtVVVLX/5dCCGEEEIIISpIaRUX8/X/qKpqURQlUSotQgghhBBC3AKNdBUrj9IqLk0URUm79n8FMFzbvj4d8u05V6oQQgghhBDitlLarGJFVwkSQgghhBBCCBcrrcVFCCGEEEIIUZFkVrFy0VR2AEIIIYQQQghRGqm4CCGEEEIIIao86SomhBBCCCGECynSdFAuctqEEEIIIYQQVZ5UXIQQQgghhBBVnlRchBBCCCGEEFWejHERQgghhBDClWQ65HKRFhchhBBCCCFElaeoqursz3D6BwghhBBCCAHcFk0ZWQ+3r9L3x8ZfNlbJ8+iSrmLHY+u54mPx1bqxAAAgAElEQVQqXPSBowCoZw5XciTlo4TFALAxKKySIymf9hfOkPd8r8oOo9zcvvmNJTVqVnYY5db7yjkss8dXdhjloh30BgAjFZ9KjqR8Jqpptv9kXK3cQMrLqzoAV9o0quRAyqfGlv2cbhJT2WGUW/iew2Q/3rWywyg3j5/WYPnlq8oOo1y0D78EwLaatSo5kvJpde4UAJbfp1duIOWkvW9oZYdQZoqmStYLqjzpKiaEEEIIIYSo8qTiIoQQQgghhKjyZFYxIYQQQgghXElmFSsXaXERQgghhBBCVHlScRFCCCGEEEJUedJVTAghhBBCCFeSWcXKRVpchBBCCCGEEFWeVFyEEEIIIYQQVZ50FRNCCCGEEMKFFJlVrFykxUUIIYQQQghxUxRF6aYoyhFFURIURXnTweuvKIpyUFGUvYqi/KkoSsStfqZUXIQQQgghhBBlpiiKFvgW6A40BAYqitLwhsN2AXeqqnoH8Avwya1+rlRchBBCCCGEEDejFZCgqupxVVVzgTlAn8IHqKq6VlXVrGubW4GwW/1QGeMihBBCCCGEK1Xx6ZAVRRkBjCi0a7KqqpMLbYcCpwttnwHuKuEthwErbjUuqbgIIYQQQggh8l2rpEwu4RBHNS/V4YGK8hhwJ9DpVuOSiosQQgghhBDiZpwBwgtthwFnbzxIUZR7gHeATqqq5tzqh1bZiot77dr4v/0u+iZNsaankf7rfJK/+was1rK9gaIQOvdX9LGNOP/sCLL+WufUeIujqiqTZv/CnKUrSE5Np3H9OrzzfBwN6kSXmjY5NY0vv5/Jms1/k56ZRUhQAE8Pepi+93V1etyGenWp/dEHeLdoQV5aKhdmzeHUZ1+U6fzX6NGdsBefwxgTg9VkImP3Hg4Ni8OaZXJ63PmCw9H0H4kSVR9Mmaibf8e6/GdQS4hf64bmgcdRImOgVh0UnZ6853u5LOTCvOrXo/HHH1LtzjvJS0vl5E+zOfLJ5yWe//qvv0r9N0Y7fO3gBx+R8NXXzgrXIVVVmbxxL3N3HCY5K5tGIf683b01DYJrFJvGYrUybfN+1sWf5tilFABia9ZgVNcWNA4NcEncAbWjufe1F4lu3ZKQRg1J2LCZL7r0LDWdh48PA776mCZ9e6LRaNj32yrmvvg6mVevuiDqolRVZdK0Gfz8y0KSU1Jo3LAhY157mQb165WYrn6LNg73u7u7s3/remeEakcbGY3nq2/j1qgJano62Ut/xfT9hJsq+31/mINbTCxpo5/DvOkv5wZ8A7fo2lR7cwy6O5qipqeTsXA+aRO/van4g2b/gq5hLJdeGEn2+nVOjdfuo0MicHviBTR1GkJWBpZ1y8lb+GOJ5aYSVR/tPX3Q1G+M4lcD9eolLFv+xPLbz2A2uyz2wlRVZfJf/zB32wGSM7NpFBbI2z3b0yDEv9g0FquVaRv3sO7wCY5dTAYgNjSAUffeReOwQJfE7VGvLhEfjsXrzuZYUtO4NPtnkj7/qkx5p1qPbtR84TmM9evbrrt79pAw7GmsJhded69RVZXJv29h7sZ/SM400ahWTd5++F4ahAWVmO7rZetZvecoZ5NTUVWICqzOU3ffRfcWN473/h9x+0+HvB2oqyhKFJAEPAoMKnyAoijNgElAN1VVL1bEh1bJiovGx4eaU6eTe+wYF154BrfwWtR47U3QaEj+71dleg/vhwegDSr5S+IKk3/+lQkz5/Ha00OJDg9l+i9LePK1d1n6/dcEVK9WbLqMzCwef/ltjAYPxrwwAj8fb46dPI3ZnOf0mLW+vjSa/zOmo/EcGvIUHpERRL3/LmgUTn38aYlpgwYPpPZHH3Dm24mcGDsON19ffDu0Q9G6MKsZPNG+8CHqudNYJ38I/jXRPDgMjaJg/W1m8el0epS296OePAqJh1HqN3FdzIW4+/rSdsFc0o8cZfvjQzFGRhI79j0UjYbDH40vNt3JmbO5uGat3b7gHt2oO+oFLq5e4+ywi5iycS8T1+9m9L0tifL3ZcaWAwz7cSWLn32QAC+jwzTZeRambtrLg03rEtf+DhRg9vZDPDZtGbOf6kVsCTceFSUkNoZGPe4jcet2tDpdmdPFzZ1OUP06zBz+AqrVyoPjxzJy0Ww+79jNidEWb/K0H/lu6jReH/U80ZERTJs1h6HPvMhv82YR4F985XHu9ClF9o186TWaN7nDmeECoHj74PPfqeSdOEb66y+iCQvH84XRoGgwTS5bxVvfux+agMop+xVvHwInTcN8PIHLLz2HW3g4fq++AYqGtG//U6b38HyoP9pA19wo2zF6oXvzU6xJJzF/9X8ogSG4DRoJGg15v/xQbDJt6y5oAkOw/DYH9fwZlFrRuPV7Ek14NOb//st18RcyZf0uJq7dyehubYgKqMaMTXsYNm0pi198hADvYsoes4Wpf/3Dgy1iiOvU3Fb2bN3PY5MXMvvph4h18oMTra8vMXNnYzoaT/zQ4egjI6j13hjQaEga/1mJaQMGPUrEuLGc+24ip8eOw83PF592bVHcKucWb8ofW5i4ahOj+3QhKqgGM9ZuY9g3P7P47eEE+HgVmy4zO5e+dzWmdrA/Wo3C77uP8Or0xWg0Gu5vFuPC30CUhaqqeYqiPA+sArTAD6qqHlAUZSywQ1XVJcCngBcw/9q6NadUVe19K59bJSsuPgMGoug9uDDqOdTMTNiyGY2XF9WefYGU76fY9pVA4+ND9Rdf5uqXnxHwwUcuirqonNxcpsz5lRED+/FYX9sT26YNY7h7cByzFi3jpaceKzbtpNnz/5+9+w6PomobOPyb3SSbzaYBCYEECAmdgHREmiDFACKiAiqIKEWwACrY4LWg2Mvra6FLERAEld57lxa6lJDQIbT0bNrufH9sSNhkN83sJvg993VxmTk7Z/bJcXJmzswppGdksHjS17jrdAC0auL4GweAys8NQOvuzt/PD8WUlATbtqP18qLamNe5/MMkS5oNLuXLETLhfc6Oe4+YufOz02+tXuOUuO9Q2nUHVx3m6RMh1Qgcwuzugab707Dh96w0G4zJmN58ynKM9o9AKTVcgp8fiMbdnX3PDSYzMQnYhquXJ7XfHEPk9z9mpeWVeuUqqVeuWqXVfuM1Ek+fIeHYcSdEniMtM5PpO48wtO199G9peVrWuEpFuny3iPl7/2bUQ81s5nN30bJ2ZB989LrstFahgXT//nfm7fubT3q1c3jsR5av5vCyVQAMWzQHz3xu8u8IadWSsPDOfNU+nMjtuwCIu3yVt/dupm6nDpzcuMWRIeeRlpbG1Fm/MGzQQAb06wNA4/sa8tAjjzP3t8W89tKLdvM2btjAavvIsePExsXxSHgXh8YMoOvdF3Q6kt4ejZqSDPt2o3gY8BjyEqlzf7ak5UPx8sbjxZGkTPovnu9OcHi8uXn2eQrFXcfN119FTU4mbQ9oDJ54D3+FxFnTC7x2KV7e+Lwymvj/fU35DyY6KWoLbaee4KYj47v3ITUFOAB6D1x6P0fmigVZaXllrvgVEuNzEk4ehox0XF94g4wKAXArxjm/QJa0jEymb41g6INN6f9AQwAaVwugy5dzmb/nKKO62B477O6qZe2YAdZ1T40qdP/2V+btOconTzi2p0PFgQPQuLtzZvAwzHeuu56eBI15jas/Trak2eBSvhzVPnyP8+Pf58a8X7PTY1evdWi89qRlZDJ9/R6GdnmA/g82B6BxSBBdPviJ+dsOMOoR+0Mc3n6is9V2m3qhRF69wbK9R6XhUkapqroKWJUr7b27fu6cJ9M/VCanQ9a3a49x5w6rSj5p9Uo0ej36Fi0LzF/u1dGkRhzEuGe3I8MsUMTxkyQlp9CtQ9vsNA+9Ox0faMG2vQfzzfvHmo080a1LdqPFmco91JHYLVutGig3lyxF66HHp3Uru/n8Hu0JwPWFixweY3409Zuh/n3QqoGiHtiG4uaOUrNhKUZWOAGdHuLGpi1WDZTLfyzFxUNPhda2u/HY4urri3+H9lz+/U9HhJmviIvXSUrLIDwsJDvNw82VDrWrsj3ykt18Wo3G6sYBwE2rpWZFX24nO6fLg6raHFuYrwbduhB/LSa70QJwbt8BbkRFE9bN8Tf8uR08fJSk5GS6demUneah19OxfRu27yxavbhy3QZL3nZtC975H3Jr1ZaMv3ZZNVDSN6xGcdfj0rR5gfk9hr1C5pEIMvbtcWSYdrm3bUfqLutrV8qaVWj0enTNC752+bwyirRDB0n9y/nxa++7H/PRfVYNFNOezSg6dzT18nmIc3ejJYv5XCQAirdvicdZkIgL10hKSye8YY3sNA83VzrUrc720xfs5rNZ97hoqVmxHLeTHF/3+DzUgfgtW60aKLeXLkOr1+P9gP3rbvmelu7MN39b7OgQCyUi6hJJqWmEN6mXneahc6NDg1psPxFV5OP5GvRkmEwlGWKZoShKmf5XVpXJhotbSCgZ0dYnuOnqVcwpKbiG5D82xK12Hbwee5xbX33myBALJerCJbQaDcFBla3Sa1SrQvRF+zdvl67GcCsuHm9PA8PemUDDh5/ggcef5dOfZpDuhD7D+lo1MZ45a5WWdvkKppQU9DVr2s3n1bQJxsizBDzzFC0i9tH6UjSNVi/Hq7ntp+sOE1AFYnKVb+wN1LRUy2dlnGetmiSdibRKM16+TGZyCp617Jd/boGPPoLGzY3Lfy4t6RALFH0zHq2iEFze2yo91M+X6Jt5b3Tyk55p4viVW9Twc/5NUGFVqluLmJOn86Rf+/s0lermP6bEEaLOnUer1VK9WlWr9Boh1Yk6d77Qx1FVlTUbNvHQg+3Q691LOMq8tMEhmM5HW6WZY66hGlPQBudf92tr1EbX4zGSf8i/W40juYaEkhFtHb/p2lXMxhRcq4fYyZWVt1ZtDL16E//NP16frViUwKqoVy5aJ966jppmRKlcrUjH0tQKQzWbUK9eLHjnEhZ9Iw6tRiG4go9VemhFX6Kzxs0VlqXuuUGNiva7dZcUfc0apEZaX3fTs6677jVr2MkFnk2bkHo2Cv+n+9H4wF80v3CW+iuX4uns626W6JhblvLPVWahARWIjrlVqGNkmswkpKSyfN8xdp6Mpm+bpo4IVdyjymTDRePtjSkxIU+6OSEBjbePjRw5Krz7HxIWzCPzgv0nK86SkJSEh16PVqu1Svf29MSYmma3EXLjtmVg4JdTZxHgV55pn73Pi888yYLla/ju53kOj9vFx4fMhLw3l5lx8bj42i9/t4r+6GvWoOprozj38SecePZ5TCkphC2Yi6u/48cmZPPwRE2x8Vo9JQnFw37/2rLC1deHjPi8539GfByuvoW/eQ98vBdxh46QfLboT7n+qXhjGh5urmg11lWMj94NY0Ym6UV4gjZl+2ESUtN4smmdkg6zxHiU8yUlLu/fTEpsHB7lnN/gSkhMtFn3+Hh5Y0xNLfQDkP0Rh7gWc50eD5f4236bFG9v1CQbdX9iAoqXt40cOQxvvEPq7wswX3L+zfIdGi9vzMW8dvm+/R+SFswn82IpXbs8vGzXm8lJKIYi1Js+5XDp1R/zzvV2u5c5kt26x11nqXsyi1D3bDlAgjGNJ5vXK3jnf0jr40NmQt5zx1TAdde1oj/uNUIJHD2SixM/5cxzL2BOSaHO/Dm4+Dnxupsl3piKh84tb/l7uGNMzyiw/A9HX+a+0Z/T6q1vGTd3Je880YXOjZz/8EeUXXbHuCiKUldV1ZNZP+vunsJMUZRWqqo69l22re4aimI7PYuhWw9cQ0K49rL9/tuOoqoqprtm/rjzks3W2zY1+zPbr+LudFWpVb0aH73xCmAZ35KcYmTK/MW88tzT6N0d3IXMVjEXUP5oNLh4enJyyHDiNm8BIHHffloc2EPlFwZxoYABhg6nKNiZYrzMsdldqaDyv4suoCJ+rR/gxIcfl3BkeamqiumuuPI999U7+xTuNfTW0xeZsv0wb3ZtSYhf/jd+pc7G/xulCP/Piv+1Kqa7GoJ36hVb9cud86qw5b9izXp8vL1om09XlRJn89TPvxzdOndDWy2ExDGvODCwQirG364+vDuu1atzc+RwBwZWCDZjLMI5rHXB7ZX3UVONZMz9qURDs8Vy3b2r7sk6rfNbXKKwPWC2njzPlC0HebObZYC/UxSn3lc0aD09iRw2gvjNlhn0EvcdoPG+3QS88ByXv/jaQcHmV/626h7rfeypFejPb2MHkZCSyrbjZ5m4aB2e7m70aB5WUmGXHWV8AcqyKr/B+fOBO+/ndt/1M8BPubat3L3a5pQpUyjqszpzQgIaG0/XNF6eNp9mAeDiQoU33iR+xjQUjQbFywuNp+UpkaL3QPEwFDiw85/Ye/gYz70xPnu7RaMGdHuwDckpRkwmk9WTz8SkJPTuOlztzPjh42WJu2Vj6/EYrZrcx/ezf+XClavUCa1e8r9Elsz4eLTeecvfxduLTBtvArLzxVlew8fvyulDb0pKIunIUTxqO/GJSUoS6A150/UeDj0HSkpGXDyuPnnL39XLm4z4wnWzCuz1KCgKl/9cVtLh5bHv/DUGzc5ZDLdFcCXCw0JITsvAZDZbPXlLSE1H7+qCq7bgl71HL9/g9cWb6dusDgNble2LVkpsHJ423irqfX1svokpSXsPRDDwxZezt1s2a0J4504kp6TkqXsSkhLRu7vj6lrwvCyZmZms27SFrg91xM3V1SGx56YmJKB4euVJVwxeqEmJtjNpXfB45Q2Mc2eARoPi6ZX9hkBx14OHB6Q458m/OdHOtcsz/2uX72tjSZg53RK/lxcag6X+UvR6h1+7sqUkgq03K0X4ftfhb6MEBZP+0UhLPexg+6KvMGhGTh3XIiSQ8AY1SE63VfekZdU9WluHsnL00nVeX7iOvi3qM7CNcyZpMdm57moLed1N2JXzLNmclETykaPoa9cq+UDvsi/yAoP+lzMRT4ua1QhvUpfktLS85W9MRe/mWmD5e+jcaFDN0r2+dd0QElPT+HrZln9nw0UUS35XL8XOz7a2reRabVON+q5oT9rTo6NwC7Xuz6ytVAmNhyHP2Jc7NHo9LpUrU+Gtd6nw1rtWnwV8/V8yLpznogMHyYbVrsGin3J+T4OHnus3b2Mymzl/5SqhVXPGVkRduExIVftjLaoGVrJ5Y3HnaalG49gefsYzkXjUsu5T6xZYGa3BgDEy0k4uMJ6ORDWbURTF+qGpohR+DYOSEHMJJaCKdQy+fig6fd6xL2VQ0pnIPGNZ3AMDcfE05Bn7Yk/Q4724vWcvqVfyrAVV4sIq+/Hb0JzZDQ1ursQkJmNSVS7cTrR6UxJ9M65Qb07O3YpnxPz1tAoNZFw3Jz7tL6ZrJ8/Qtl3rPOmV6tbm0JIVDv3usHp1WPxLznS1Bg8PYq7fwGQycf7iJUKrB2d/FnXuvNV2fnbv3c/t2Fh6POy8yQVM56PRBluPBdFUrITi4YHpvO26X9Hr0QZUwjDqLQyj3rL6zOvjrzBdukBcn+4Oi/luGdFRuOQah6kNyLp2nYu2mUfR63GpVJlyY9+h3Nh3rD7z++JbMi6c51rPhx0W8x3qlYtoKlfDqiNPeX8Udz3q1YK7r7n0fxlN0zakfz7WaWNbwoL8+W3EE9nbBp0rMQnJmMwqF27FW70pib4RR4h/wd02z92MY8SclbQKrcK4no6fkOIOY+RZ9DVtX3dzj325W+oZy3U3712aAmbHvu0Nq1qJ38YOyt426NyIiUu0lP+NWEICcmZkjI65ZbVdWPWrBPDnniNkmEyFanSKf7/8Gi6qnZ9tbZco4/Zt+Lww2OpJk2d4d8xGI8Z9e23mMaekcGWQ9fTCWj9/Ar76ltvffo3xL8fOMObp4UHDOtZPN4ICKuJp8GDt1l2MGNAXAGNqGpv37KNvj652j+Xm6krrpo35K+KIVfruiCPo3XUEB1a2k7NkxG7aTNBLI9AaDJiyZsfx7/UophQj8bvs9xC8vX4D1ca+jk+b1sRutKwbovXywvO+hlyeNMWhMd/NfOIAmk6Pg04PaZbZYJRm7VDTU1EjjzotjuKK2biJmq+MQOtpwJRkKf+g3o+SmWLk1q6Cz2N91SqUb9GcI2PeKnDfkmDQudIg1/oqgb4GPHWurD0RzfD2jQEwZmSy5fRF+jTLf6zKjcQUhs5dS9XyXnz1RIc8faXLomOr19Pjvbeo0aYVZ3da/kaqNWuCf40Qjq9e79Dv9jQYaFjfug9+UOVKeBoMrNmwiZeGPA+A0ZjK5m076ft4r0Idd+Xa9fhXqMD9zZ03MDZ9zw70/Z+3ekvi1jkcNdVI5sH9NvOoxhTiX3reKk1TwQ+vj74kedJ/ydz/l8PjviN1x3a8Br1gde3yeNhy7Urbb/vapaakcH3wQKs0jZ8ffp9/Q9x335DmpBnSTEf+wqV7P3DXZ8/IqG3VETUtFfPfh/PNq+35NNquj5Hx/QTU08ecES5guVFukGtxyEBfLzx1bqw9FsXwjpYB6sb0DLacPEefFvkvZHgjIZmhs1ZQtbwPX/Xr7NS6J37TFiqPeBGNwYA567pb/tGemIxGEnbbPwfiNmwkaMxreLduTXzWOl5aLy8M9zXg2uSpdvOVBIO7LvvtyB2B5X3wdNexNuIkw8PbAFnlfyySPm0aF/k7IqIuUcnX61/ZaCnLM3eVZfk1XKooivI/LO34Oz+TtR3kyKASfvsV7wHPEvDdD8TPmIZL1aqUe/lV4ufMtJpmsurq9Rj37eXme+PAZCI1V6PGJdASZvqZU6QdtW4EOIPOzY2hTz3BpLkL8fY0EFqtCrMWL8VsNjPgsZwV2Zes28S4L79n3dwpBAVYKuGXB/aj/6h3eOeL7+jxUHtORZ1j2q+/89KzfXFzc2y3jauz5xI45AXqzpzGpR9+wj04mGpjX+fylKlWUyQ327OD+N17iHzNslp70uEj3Fq9hlrffsm5jz8l4/Ztqrz8EmpGJld+nu3QmO+mbl8FD/ZEM/Rd1PWLwa8Smu7PoG5aYjVFsvb9qahnjmGe/7/sNKV+M3BzR6lieeqrNLZUvOr50xB7wynxn585h9Chg2k5ewZn/vcjhuBg6rw5hqhJU6ymSO60bxc3d+3m8Kg3rPIHPf4Y5owMrixb6ZR4bdG5uDCkzX1M3nYIb3c3Qvx8mb3nGGZVzV7XBWDp4TOMX7qDNSP7EOTrSWpGJsPmrSPBmM74bg9wOiZn1XlXrZb6lYv+xK6oXPV6Gna3PFjwDQrE3duLpk9YbvaPrlpHhtHIhDOHOLN1B78MsYypiN6zl+NrNjBozhR+HzM+ewHKM9t3OX0NFwCdTsewQc/y0/SZ+Hh7WRagnLsAs9nMs1nrugAsWbGKdyd8wvqliwiqnHMDkp6ezoYt2+jds7vD3/DeLe3P39D36Y/Xp9+ROncGmsAqeAx+CeOvc6y6K/kuWkVGxH6SP3kPTCYyI/ZZHUdTKRAA09kzZJ5w3sOKpEUL8HpmAH7f/I+EmdNxqVIV7xEvk/jLLKtrV6Xla0k7sI/YD8aDyZSnUaPNunZlRJ4m3UnXLtPG5bh0fRzXUR9iWrEApWJlXHo/h2nNYqtB9m5f/YL55GEyp1t6GGgeeAjXvkPJ3LYGNfYmSo2cRrR6/YrN6ZIdSefqwpAHmzB58wG89TpC/H2ZveMwZpXsdV0AlkacYvwfm1nzen+CynlZ6p7ZK0kwpjH+kXacvpYzA5ari5b6gY5dgPL6nLkEDH6eWjOmcvXHSeiCqxE05jWuTZlmNUXyfbu2kbh7D9FvvAlA8uEjxK5ZS8g3X3Jp4mdk3L5N5ZeHo2ZmEjNzjkNjtkXn6sKQLq2YvGYn3h7ulgUoN+211P3tc6Y0X/rXUcbPX8ma90cQVN6Hy7fjGT93JT2a16dKBV9S0jPYcPgUqw7+zXv9HP/GUdw78mu4jL3r59yPumw/+ioh5oQErg5+Dr9x7xHw42TMiQnEz5lF7I+5Vk7WalHKeCt82NNPoKpmpv76O3EJiTSoXZOfv5iAX/mcV9Zms2Vg/90Dsu+rW5ufPh7Ht9N/YcWmbVTw9WF4/z4Me/pJh8dsio/n6JNPUePTj6k/ZxamhHguT5nGhS+/sdpP0WpRct3UnHppJCHvjyfkw/fR6PUk7tvH0Sf7YSrk2IwSYUzG9P04NH2Go3nxPTAmo25ainnVfOv9NFrIFb+m30soFXJW3dYOsXTdMP3yLepfGx0eOkBGfDy7Hu9Lw88ncv+82WTEJ3B28lRO5ZrcQHFxsXn+B/Xuxc1tO0i/VbipJx1laNv7MKsq03YcIc6YRligH9OfDcfPU5+9j1kla2C/5dy/lWzkVFZjZcSv1m8qAn082TC6r8Pj9q7oz7DFv1il3dkeV70Bt85fQOuiRZOr7Kc/9Tx9vv2UgT//iKLRcHTFWhaOHEtpGfb8QMyqypSZc4iLj6dBvXrM/Ok7/CqUz97HbLYM7M899nfbzt0kJiU5tZsYgJqYQMKrgzG8MQ6vL39ATUzEuHAOxum5Bnpr8/7tlgVqYgLXhz1PuXf+g9//JqEmJpA4dzYJk36w2k/RuqBoyti1KyWJ9E/H4PLcSFxfnwgpSZjWLCbzD+uHTorGut7XNrTcjLq0D8elfbjVvhlTP8e03fkLIQ5t3wSzWWXa1oPEpaQSFuTP9Ocfwc/TI3sfc/bA8qy6JymFU1mNlRG/WK2nR6CvFxvG2l8wuiSY4uM52fdpqk+cQO3ZP5OZkMC1qdO5/NW3VvspLlrL+X+Xsy+Poup746j2wX+yrrv7OfnkU8697t5laJcHLHX/+t3EJRsJq1qJ6S8/hZ93ztjT7PLPqny89Tr8fTyZvHYXNxOS8NK7U7OSH5OG9+HBsMIvAyD+/ZTiLLZWRGpU2L05lV3occu6DOqlk6UcSfEoVSwrze64B9YusaVtzCUyX3mk4B3LKJcfVrCsgmO79TnSo7euYpr/eWmHUSzaZyzd5IYr+RPtA8MAACAASURBVE+hW1ZNVrMG4ybdzn/HssrT0ji69UCDUg6keCrsPsbFRvfuSt1VD58k9VnHrvTuSO6/bMK0+L+lHUaxaJ8cDcDeIq59U1a0zBrPZFo3q3QDKSZt10FQwDjssiJ9cNcyPc2p24x1ZbIc7T6yUhSlraIoA+/aXqwoyqasf/dujSiEEEIIIYS45+TXVexD4NW7tusAgwAD8C6wyXFhCSGEEEIIIUSO/DoJe6uqeuKu7TOqqh5QVXUbkHeifSGEEEIIIYRwkPzeuFhNeK6q6uN3bQYghBBCCCGEKDqZDrlY8nvjclJRlB65ExVFeQQ45biQhBBCCCGEEMJafm9cXgNWKoryJHAwK60Z0Bq4d6d6EkIIIYQQQtxz7DZcVFWNVBTlPqA/EIZlsvOtwHRgFPCyUyIUQgghhBDiX0TRSFex4sjvjQuqqqYBPyuK0gR4GngfiAZ+d0JsQgghhBBCCAHk03BRFKU28BSWBsstYCGWBSs7Oik2IYQQQgghhADyf+NyEtgO9FRVNRJAUZTXnBKVEEIIIYQQ/1Yyq1ix5Der2BPANWCzoijTFEXpBEgpCyGEEEIIIZzObsNFVdU/VVXtB9QFtmCZZSxAUZRJiqJ0dVJ8QgghhBBCCJH/4HwAVVWTgXnAPEVRygN9gLeBdQ6OTQghhBBCiH8fmVWsWPLrKpaHqqq3VVWdoqrqQ44KSAghhBBCCCFyK1LDRQghhBBCCCFKQ4FdxYQQQgghhBAlR5FZxYpF3rgIIYQQQgghyjxFVVVHf4fDv0AIIYQQQgjukaU7Ml/qUabvj11+Wlkmy1HeuAghhBBCCCHKPKeMcTGf2OmMrylxmvptANjgH1TKkRRP5xuXLT/EXi3dQIqrXGVMM94r7SiKTTt4AqTEl3YYxefhQ8aI7qUdRbG4Tlpl+SHpdukGUlye5QEYrniXciDFM1lNAMC8d0UpR1I8mpaPYD66pbTDKDZNww6Yfv6gtMMoNu0LH5DyZNvSDqNYPBbvAMAcsaGUIykeTZPOAPd+3X8vkOmQi0XeuAghhBBCCCHKPGm4CCGEEEIIIco8mQ5ZCCGEEEIIZ5LpkItF3rgIIYQQQgghyjxpuAghhBBCCCHKPOkqJoQQQgghhDNJV7FikTcuQgghhBBCiDJPGi5CCCGEEEKIMk+6igkhhBBCCOFM0lWsWOSNixBCCCGEEKLMk4aLEEIIIYQQosyTrmJCCCGEEEI4k0beHRSHlJoQQgghhBCizJOGixBCCCGEEKLMk4aLEEIIIYQQoswr82NcVFVl6u8rWbBmM7GJSTSoGcK4Ic9QL6Ravvne+d8MlmzemSd95fcTCa1S2VHhZjPUrkWdTz/Gp3kzMhPiuTz3V6K+/AbMZrt5Qse+Tuibb9j8LPLjTzn33Q+OCtcuVVWZMnsev/6xlNj4eBrWq8v411+lXu1a+ear06qDzXRXV1eObV/vgEhtU1WVqXv+ZuGhSGKN6TSoVJ53OzWhXkC5fPPtOneNP45Ec+jKTa4kpPBS6zBeadvASVHnUFWVKT/P4tdFvxMbF0/D+vUY/9YY6tWpnW++Ok1a2kx3dXXl2N68fxclqlJVtP1GoITWhZRkzDvXYl45H1T75z5aFzS9BqKE1EWpVgvFTUfGiO6OjbMAqqoyZeZsfl38J7FxcTSsX5/xY18ruOybPWAz3dXVlWN7tjkiVCv+NULpMnYkoa1aENigPpHbd/FNxx4F5nP39qbvfz+j0WM90Gg0HF2xloUj3yT59m2Hx2yLqqpMXb6RBRt3EZuYTIPQaox79jHqBQcV+hgb9h/l1e9mERZShcUTXnNgtHmpqsrUP1azYN02y7WrRjDjXniKeiFVC32MDXsP8eoXkwgLrcbiL8Y5MFprlnrzBAsjIok1plnqzc7N8q03TWYzM/eeZMvZK5y9GQ9AWKXyjGp/Hw0rV3BK3EqV6rgNHo2mdgPU5CRMG5eTsWhmvtddTY26uDzcG029Rijl/VBvXidzx3oyl8yDjHSnxJ2bqqpMXbKWBeu3W879GtUYN6gP9aoX4dzZd5hXv55qOXc+ecuB0d7lX1L3O41Mh1wsZb7hMu2PVUxatJyxz/UlJKgSs5at44X3v2LZdx/hX84n37yhQZWZ+OoLVmlBFf0cGS4ALj4+NP19AUmnznB44PPoq1en9ofvoWg0nP30C7v5Ls/9lZubtlilVez+MNVHvsLNjZsdHLVtU+fM56eZc3jzleGEBldj5q+LGPTqG6yYPxP/CvYvRgun/5gnbfiYd2l6n3Nv/qf99TeTd59gTIdGhJT3Yvb+0wz+bQtLnw/H31NvN9/2qKucuhFHq+AAVp+84MSIrU39eTY/TfuZN0e/Smj16sycO59Bw19mxaJf8fezfy4vnD0jT9rwUW/QtPF9jgwXPDxxGfUJ6rULmCZ9hOJfGc0TQ0Cjwbxsjv18bjo0bR5GPXcaNepvlLqNHRtnIUydOYefps/kzVGvEFo9mJnzFjBoxEhW/DYPf798zv1Z0/KkDR89lqaNHFz2WQLD6tKge1ei9+xD6+ZW6HxDF84ioE5N5g55FdVspvfnExi+ZD5ftw93YLT2TVu+iUlL1jP26Z6EVK7IrNVbeeGzySz7dCz+vt4F5k9Lz+Dz+cvw8/FyQrR5TftzDZN+X8XYZ5+wXLuWb+CFCd+y7Jv3C7x2QVb8sxbhV4jftaRN23OCybuOM6ZDY0IqeDN730kGL9zE0he62603UzNNTN9zgt4NQxnaqj4KMP/gGQbM28D8AV0Iq1TesUEbvNC991/US+dI+/xtNJWCcB34CigaMhbk/Zu8Q9umE0qlIDKWzkO9eglNcA1c+w1BE1yD9K/GOzZmO6YtXcekP9Ywtv9jlnNn5UZemPg9y74ch79vIc+dX3537rn/L6r7RdlWphsuaekZTPtjFUMf70H/7p0AaFynJp1fHMu8VRsZ3f/xfPPr3d1oXKeGM0K1UmXQs2jc3TkyaAimpCTYuh0XL09Cx77Bue9/sqTZkHb1KmlXr1qlhb4xmuTTZ0g6dtwZoVvHk5bG1DnzGTawPwP6WMq6ccMwHur9FHMX/clrw4fYzdu4QZjV9pHjfxMbF88jXTo5NOa7pWWamL7nJEPvr0f/ppY3RI0D/egyZQXzIyIZ1a6h3bxjOzbmrYcsT0M2RV52Sry5paWlMXXWbIY9/xwDnuoLQONGDXmoey/mLlzEay+PsJu38X3Wv9uRY8eJjYvjkfCHHRqzpl13cHPDNOVjSDWinowAdw80jzyDed0iSDXazmhMJvONfpZjPPgIlPLFy1L2vzBs0EAG9OsDWMr0oUceZ+5vi3ntpRft5m3c0LpxnlP2XRwac/b3LV/N4WWrABi2aA6e+TSy7ghp1ZKw8M581T6cyO27AIi7fJW3926mbqcOnNy4xZEh55GWnsG0FZsY2rMT/bu0BaBxzWA6vz6Reet3MrpPtwKPMWPVFgLK+VC1YgXOXL7m6JCtpKVnMG3JGob2Dqd/t44ANK4dSueX3mXems2MfvqxAo8xY+k6Asr7UrWSP2cuOK8OstSbfzO0VX36N7O8XWwc6EeXycuYf/AMo9rbboC7u2hZO/xRfNxzGsutqgfQfepK5h04zSc9Wjk0bpeuvVDcdKR++S4YUzAf2Q96A659XyBj6TwwptjMl7FkHiTEZW+bj0egpqejG/4mGX4BqDdjHBp3bmnpGUxbuo6hj3Wlf3gHABrXCqHzq+8xb+02RvfrWeAxZizfQEA5X6oG+HHm0tUC9y8J/5a6X5R9ZXqMS8TJSJJSjHRr0yI7zcNdR8fmjdl+8GgpRpa/Cp06cmvzVqsGyrU/l6L10FOute1uJLa4+PpS/sF2XPtjiSPCLNDBo8dJSk6mW6cO2Wkeej0d27Zm++69RTrWyvWb8NC707Fd6xKO0r6IyzdJSs8gvG7O63UPNxc61Axke1T+lbmmDLzCPXj4CElJyXTr2jk7zUOvp+OD7di+c3eRjrVyzTpL3vbtSjpMK0pYc9QTB60uUub9W1Hc3FFq2W8oljUHDx+1nPt3NbQt5dem6GW/boMlb7u2JR2mTaqqFjlPg25diL8Wk91oATi37wA3oqIJ6+acBtfdIs6cI8mYSrf7G2Wnebjr6NikPtuPnCww/5WbscxYuZl3ni24geAIEafOkpSSSrfWzbPTPNx1dGx2H9sjCn4IdeXGbWYsXcs7L/RzZJg2RVy+kVVv5nTHzqk3r9jNp9VorBotAG5aLTX9fLidkuaweLO/v0krTIf/smqgmHZuRNG5o63fxH7Guxotd6jRpwFQfPLvUuwIEaejLOd+q6bZaZZzpwHbDxXi3Ll5mxnL1/POoCcdGWYe/5a636kUpWz/K6PKdMMl6vJVtBoNwZUDrNJDq1Qm+nLBTxHOXrxK82de4r4+w+j/zifsPXbKUaFaMdSsScqZSKu0tMtXMCWnYKhV+DdAAT17oHFzI+bPZSUdYqFEnbuAVquhetUqVuk1qgcTdb7w3adUVWXNpi081K4tenf3kg7TrujbCWgVheBynlbpoeW9ib6d4LQ4iivq3Hm0Wi3Vq1n3a64RUp2o6HOFPo6qqqzZsJGHOrRHr3ds+SuVqqBeu2SdGHsDNS0VpVLh+2eXtnzL/tz5Qh/HUvabeOjBdg4v+3+iUt1axJw8nSf92t+nqVQ3/zE9jhB19bql7q/kb5UeGhhA9JXrBeb/Yv4ywls2Iqx6lQL3dYSoy9ey4q9olR5apRLRhXj788XsRYS3bk5YaP5jOR0h+lai7Xqzgk+R6830TBPHY25Tw8/x3d00QcGol62vS+rNGNRUI0pQ0cpRU6chqsmE+YrzuwlHXYnJuu/Jde4EVSL6SsFvf7745Q/CWzUlrIBxwCXt31L3i7KvTDdcEpKS8XDXodVah+ntacCYlk56RqbdvPVCqvHm8/346d1RfPnaMExmlSEffsWR01GODhsXXx8yEvJW8Bnx8bj4+hb6OAG9e5Fw+AgpUY6P2ZaExEQ89Hq0Wq1Vuo+XJ8bUVNIzMgp1nP2HjnDt+g16dHnIEWHaFZ+agYebC9pcizz5uLtizDCRbjI5NZ6iSkhIsFP+XkUr/4MRXIu5To+HuzoiTGsenmC00RUyJcny2T3C/rnvXbSyjziUVfadC965FHmU8yUlLj5PekpsHB7lCl9nlZSEZCMe7m55/na9DXqM6emkZ9qv+/86EcmOo6cK1Z3MURKSU2xfuwwFX7v+OnaKHYdPMPrpXo4O06b41HQ79aZbkevNKbuPk5CazpP3OaHLtsELNTkxT7KanIjiWYSxHr7lcX1iIKZta+12L3Ok7HMnz7nvYTl38jv3j59mx5G/Gf1Uwd3JSty/pO4XZV+RxrgoilIBaA9cUFX1QEkGoqoqprtm/lBQ7nynzX0tn9k/3sCe1t0bHmx2H4+MHM/U31fywzuvlkDEBbDVXUOxk26DW0BFyrVuxZkJE0s2LjtUVcV01wXpTrnbLn/sfmbLinUb8fH2om2rFgXvXEyqqmK6q2yVXP+12jf7s7LzKrRI5X9nn0LGv2LNOny8vWnb2rF9zLPZOsWLcO47W9HO/ay6p9Blv95y7j/gpLL/J2z8/1EUxeH/3/LU/XfK30YZZ9c9dso/02Ri4i9/MrxX50IN4C8Jxbt25RP/zwsY/kT3Qg3g/6eKVG8W8dzfevYyU3af4M2OlgH+TmHzVFXspNvg4oLu9QmoqUbSZ31fgoHZZvfct1HEhTr3Zy1ieO/wQg3gd4h7rO4vdWW4O1ZZlm/DRVGUFcDbqqoeUxSlMnAQ2A/UUBRlqqqq/7WTbxgwDGDKlCkMaRtmazcr+46f4rn/5My41SKsDuFtWpBsTMVkMls9uUpMTkGvc8PVpfDtLnedG+2bNmTL/sOFzlNcmXHxuHjnrahdvL3JjM/7VNOWgF49QVGIWbK8pMOzae/BQwx8OWe60JZNGhHeqSPJKSmYTCarJ88JSUno3d0LVf6ZmZms27KNrh3a4+bq6pDYAfZdvMGgBTkzr7Wo6k943aokp2diMputnl4lpGagd9Xiqi07Lxz3HjjIwKE5g+1bNmtKeNfOtss/MdFS/q6FLP+Nm+naqaNDyz9bShJ4GPKmuxvAmOz47y+GvQciGPjiy9nbLZs1IbxzJzvnfhHLftMWuj7kpLL/B1Ji4/D0zztLnd7Xx+abmJK07+RZnvtkUvZ2i7o1CL+/EcmpaXn+dhNTjOjd3HB10do6FIs2/0ViipHH2rUgIdnS1z7DZMJsNpOQbMy6btjOW+z4j5/muQ++yYm/fm3CWzezfe1KSck3hkUbdpCYbOSxDg+QkGx52p+RmYnZrJKQnIJepyvR+PddvM6gXzflxF61IuF1q9muN9MKX28evXqL15fuom/jGgxsUbfE4s1XciIY8j7ZVzwMNt/E2OL26ng0VUNIHTfCcjwH23fiDM999F32dot6tQh/oCnJRlvnfgHnzsadJCan8Fj7++86d+6c+yV/7uRxD9b94t5U0NU3RFXVY1k/Pw+sV1V1oKIoXsBOwGbDRVXVqcDUO5vmEwWvGxFWI5hFX/4ne9ugdyfmVhwms5kL12IICcpZeyXq8lWr7SJxQgs3OTISQ62aVmm6wEBcDAaSz5wt1DECHutF3F97SbtifzBkSQqrW4fFMydnbxs8PIi5cROTycz5S5cJDc7pLxt1/oLVdn527z/I7dg4enR17GxiYQHl+O3ZnLdsBjcXYpKMmFSVC7FJVk/8om8nEFLe+VOM5iesXl0Wz52VvW0wGIi5fh2TycT5i5cIrR6c/VlU9DlCQ6oX6ri79+7jdmwsPcKd0E0MUK9dQgnINa6gnB+Kux712kWnxFBUYfXqsPiXn7O3DR4exFy/Ybvsz5232s7P7r37LWX/sPMHtxfVtZNnaGtj4oxKdWtzaMkKh353WPUqLPpwdPa2Qa8j5na8pe6PuUnIXX39o65cJySwoq3DABB97TrXbsfT7pUP8nx2//DxfD78GR5t06xk468RzKLP3rkrfndibt+5dl0nJKhSTvyXY6y288R/5RrXbsXSbsjYvPE/9xqfj3yeR9uX3Nu7sIDy/DYwp24wuLkSk5Riu968Vbh689ztBEYs3kqr4ADGdS7Zss6P+fJ5NEHWf5tKhYooeo88Y19scR00Em3zdqR99Bqqk8a2hIVWY9HEN7O3rc+dG4QE5ozvjbocY7WdW/TV61y7HUe74e/k+ez+wWP5/OXneLSd7fW9SsK9WPeLe1NBDZe7O3J3AqYBqKqaqChKPisKFZ1Br6dBzRCrtEB/Pzw99KzZtZ8RfSx9No1paWzZd5g+XR8s0vFT09LZHnGUsNDC3XT8E7c2bib45eFoDQZMyZYnDQGP9cSUYiR2V8EzErlXrYJvi2b8PTZvBeQongYPGtazfjIWVCkAT4OBNRu38NILAwEwpqayeccu+vZ6pFDHXbluI/4VynN/U8dOcWjQudKgsvU6AYE+BjzdXFl76iLDW1ve+hkzMtly9gp9Gjl/muz8eBoMNAyrb5UWVLkSnp4G1qzfwEtDBwNgNKayedsO+j5RuNmSVq5Zh79fBe5v7pwbCPX4fjRdngCdHtIsT7w1zdqjpqeinimbMwF6Ggw0rF/PKi2ociXLub9hEy8NeR64U/Y76ft44cYerFy7Hv8KFbi/edOCdy5lx1avp8d7b1GjTSvO7twDQLVmTfCvEcLx1Y5dMNagd6dBqPXg3cAK5fDUu7Pmr8OMeMzS8DOmpbMl4gR9Otq/ce/fpS2dm1lPRz1t+SYu3bjNhy88SWg+N37/KP6a1a3j96+Ap4c7a3YfYMSTPXLi33+EPl3szy7XP7wjnVtY15XTlqzh0vVbfDisf4kvnmypN62ny86pNy8wvLWlLC315mX6NKpp6zDZbiQZGfrbFqr6evLVo63zjNNwJFPEHlwffQbc9dkzW2nbdEJNS8V0IiLfvC69B+DS7QnSv3kP88kjzggXyDp3aljfkwT6lbec+3sOMuJxyzgtY1o6Ww4epU+nfM6dh9vTubn1VNXTlq2znDtDniY0nwZzSbgX6/5S58S/j3+TghouFxVFeRW4BDQF1gAoiqIHHN73QefmytDHuzPpt+V4GzwIrVKZWcvWYVZVBvTIeYK/ZPNOxv8wk7WTPiOooh+JySmMmPgdPR98gGqVKxKbkMTs5euIuRXHt2Psr31RUi7N+oWqQ1/gvlnTOf/9T+iDqxH65htcmDzVaork1nt3ELtrD3+PHmOVP6B3L8wZGVxf7tgnnQXR6XQMG/gMP/08Bx9vr+wFKM1mM8/2yVlDZ8mqtbw78XPWL55PUOWcyjE9PZ0N23bQu0c4mlL4A9W5aBnSqi6Td53A290tayG1U5hVNXtdF4Clx6IZv3ofa4b1IMjH8qr7cnwyx65ZVgzPMJk5eyuBtacuond1oX1oyd482I1fp2PYoOf4afoMfLy9LYsgzv0Vs2rm2ax1XQCWLF/Jux9+zPplfxAUmBNbeno6GzZvpfejjzit/M3bV6Hp+CjaF8db5u73q4SmR3/MG5dYTZPp8uF01DNHMc3N6SahhDUHNx1KVUujUmnSBgD1/Bm4XfBMUiXJUvbP8tP0mZZzv3owM+cusJz7Weu6ACxZsYp3J3zC+qWLCKqcq+y3bKN3z+5OP/dd9Xoadrc8RfcNCsTd24umT1gaW0dXrSPDaGTCmUOc2bqDX4a8AkD0nr0cX7OBQXOm8PuY8dkLUJ7Zvsvpa7hAVt3/yENMWroeb4Oe0MCKzFq9DbNqZkDXnJu3JTv2M37aQtZ+/Q5BfuUJDvAjOMC6y9uf2/YRm5RMy3r533SXePyPhTNp8UrLtSuoErNWbLDE3y1nkpIlW3Yz/qc5rP3xY4L8KxBcuWKe2aT+3LKb2IQkWjao45zYXbQMaVWPybuOW+rN8nfVm81y1Zur/mLNiz0J8jGQmpHJsEVbSEhNZ3yXZpy+njPNsKuLhvoBjl2AMnPdUly7P4lu7CdkLJmHEhCIa5/nyVy+0GqQvfv3CzCfOET6pM8A0Lbtglv/4WRuWol6+yaaWjld280xl21Ol+xIOjdXhvbqyqQ/VlvOncAAZq3aZLnveTjnge2SbX8xfvJc1n73geXcqVQxzyx2f27dQ2xiMi3DHD8z4L+l7hdlX0ENl8HABKAz0E9V1Tt/wa2AmY4M7I6hj3fHbDYz7Y9VxCUm0aBGdWZ88AZ+dw0+yz3Azc3VlXLeXkxetJxb8Yno3FxpXKcGcz5+K89bHUfIjI/n4OP9qPPZRBrNnUlmQgIXJk8j6ouvrfZTtC4o2rx9Tiv17sXt7TvIuHXb4bEWZNjAZzCbzUyZPY+4hAQa1K3DzP99jV+FnIuQ2WzGZDLnWT9i2+6/SExKdvpsYncben89zCpM2/M3canphFUqx/S+HfAz5ExNa1axGqAKsPfCdcatzlmrZu2pi6w9dZFAbw82DHfejC3DXngOs2pmys+ziYuPp0H9usyc9AN+FXKekpqzBperuUZGbtu5i8SkJOd2VUpJIvO/76B96iW0I94HYzLmTUswr5hnvZ9Wm+dpk/bpl1Eq5DwRdxk2DoDM2d+g7tng8NBzG/b8QMyqypSZcyxlX68eM3/6Lte5n1X2ucaebtu52/lln8W7oj/DFv9ilXZne1z1Btw6fwGtixZNrrpn+lPP0+fbTxn4848oGg1HV6xl4ci8XZacZWjPhzCrKtOWbyIuKZkGIVWZ8daLVquBq2azpe4vg2N/h/YOt8T/5xpL/KHBzPjPaPzumjQg+9pVxgYvD21V31Jv7j6RVW+WZ3q/jvgZ9Nn7mLMH9ltiv5WSyqmsxsqIxdusjhfobWDDiEcdG3RyIqkfjsZt8Gvo3v4cUhLJXPkbGb/9bL1frrpH28gyaYzLQz1weaiH1a5pP0zEtGW1Y+O2YWivrpZzZ+k64hKTaRBajRnvvmp97pS1c/9fVPeLsk0pzmJlRVSoMS5lkaa+pdW/wT+olCMpns43slZbjnXOyrklrlxlTDPeK+0oik07eAKkOHZgs0N5+JAxontpR1EsrpMsK8eTVPqN/2LxtDSOhitlayxWYU1WLdPBm/eW7lvj4tK0fATz0S2lHUaxaRp2wPTzB6UdRrFpX/iAlCeds2BrSfNYvAMAc8S9ecOtaWKZuv0er/vviem6Mt/qV1aanTa5fL6wTJZjQbOKLSef9ryqqg5+hCKEEEIIIYQQBXcV+8opUQghhBBCCCFEPvJtuKiquvXOz4qi+Gel3XB0UEIIIYQQQvxryQKUxVLgdDeKoryvKMpN4CRwWlGUG4qi3LsDD4QQQgghhBD3nHwbLoqivAa0BVqoqlpBVdVywP1Am6zPhBBCCCGEEMLhCnrjMhB4WlXV6DsJqqpGAQOyPhNCCCGEEEIIhytocL6rqqo3cyeqqnpDURSHL0AphBBCCCHEv46McSmWgt64pBfzMyGEEEIIIYQoMQW9cWmkKEqCjXQFcLeRLoQQQgghhBAlrqDpkLXOCkQIIYQQQoj/FzQFTuwrbJBSE0IIIYQQQpR50nARQgghhBBClHkFjXERQgghhBBClCSZVaxY5I2LEEIIIYQQosyThosQQgghhBCizJOuYkIIIYQQQjiTdBUrFnnjIoQQQgghhCjzFFVVHf0dDv8CIYQQQgghsCySXuZl/ufZMn1/7PLRL2WyHKWrmBBCCCGEEM4kXcWKxSkNl4/cyzvja0rcf1JvA2BaMaWUIyke7SMvAnAsJKSUIymeBtHRZL79VGmHUWwuny3gUNXqpR1GsTW+eA7TD2NKO4xi0b7yFQC3HmhQypEUT4XdxwAw711RypEUj6blIwAMV7xLOZLimawm8KW+QmmHUWxjjbeIaVG/tMMotoB9JzDtWFzaYRSLtu2TACR0blrKkRSP94aDAJgWfVPKkRSPts/rAidDLQAAIABJREFUpR2CcDAZ4yKEEEIIIYQo86ThIoQQQgghhCjzZIyLEEIIIYQQTqRo5N1BcUipCSGEEEIIIco8abgIIYQQQgghyjzpKiaEEEIIIYQzyXTIxSJvXIQQQgghhBBFoihKuKIopxRFiVQU5W0bn+sURVmY9flfiqJU/6ffKQ0XIYQQQgghRKEpiqIFfgS6AfWBpxVFyb2A1GAgVlXVmsC3wOf/9Hul4SKEEEIIIYQzKUrZ/lewlkCkqqpRqqqmAwuAXrn26QXMzvp5MdBJUf5ZHzlpuAghhBBCCCGyKYoyTFGU/Xf9G5ZrlyDg4l3bl7LSbO6jqmomEA9U+CdxyeB8IYQQQgghRDZVVacCU/PZxdabE7UY+xSJNFyEEEIIIYRwpnt/VrFLQNW7tqsAV+zsc0lRFBfAB7j9T75UuooJIYQQQgghimIfUEtRlBBFUdyAp4BlufZZBjyX9fOTwCZVVeWNixBCCCGEEMI5VFXNVBTlFWAtoAV+VlX1uKIoE4D9qqouA2YAvyiKEonlTctT//R7peEihBBCCCGEM2nu/U5PqqquAlblSnvvrp9TgT4l+Z1ltuHiV7cO4d9+RpX7W5AaF0/ErLls+/hzVLM533z+9erS5cuJVGt9PxkpRk78sZQN77xPRnKykyK3pqoqUzfuZeGuw8QmG2lQrRLvPtaRekEV8833/ZpdbDh6hiuxiaiohPiX54UOzenWpI5T4tbVrEnlDz7Ao2lTTAkJxC5cyPXvvoN8yt81KIg6O3bkSY9bvpxLI0c6Mty8KgahefR5lGq1IDUZdd9mzBsWQ35vKLVaNF2fsuSpEori6kbm2//44UCx6GrVpMqEDzE0s5T/rV8XcO3b/MvfrUoV6u/OW/6xy5Zz/uVXHRmuTaqqMnV/JAuPnSPWmE6DAF/ebd+Aev4++ebbdeEGf5y4wKFrsVxJNPJSy9q8cr9zznsAbfVQDG+8i0uDRqiJiaQu/x3jjEn5lr0VRcHn5wW41A0jYczLZOzc6tiA7VBVlanLN7Jg4y5iE5NpEFqNcc8+Rr3g3JO+2Ldh/1Fe/W4WYSFVWDzhNQdGm8O/Rihdxo4ktFULAhvUJ3L7Lr7p2KPAfO7e3vT972c0eqwHGo2GoyvWsnDkmyTf/kfdqYusQt06dPrmMwLvb05afAJHZv7CrolfFHjtqlCvDg99MZGgrGvX6T+WseVd51+7tCE18Bo7DreGjTAnJmJcupjkaT8V6fwvP/s3XOuFEfvaCNJ3OP/8V1WVqau2snDzXmKTkmlQvQrvPtODetUCC32MDQdPMPLHeYQFB7LovZcdGG0OTbUQ3F95C239hqhJSWSs/pO0X6bmW/aa4FDch7+OJqQWircPatwtMvfvIW3WJNTbN50Sd26qqjJ1awQL954gNiWVBkH+vPtIG+pV9rObx2Q2M3PHEbacOs/Z67EAhAX6M6pLCxpWyf9+Sfz/UiYbLu6+PgxY9Qc3Tp5iYZ8BlAupTpfPP0LRKGz54BO7+XTeXgxYs4TbkWf5fcBg9BXK03niB3hVCuC3vs867xe4y7RN+5i8fg9jerYnpGJ5Zm89wODJi1k69jn8vQ128yWnpfNYizBqBFRAq1FYd+QMb8xdiUaj8HCj2g6NWePtTfW5c0k7c4bzw4bhVq0alceNA42G619/XWD+qxMnkrJ/f/a2KTbWkeHmpTegHTIONeYy5jlfQYUAND0GoFEUzOt+s5/PVYfSsiPqxbNw/jRKzQbOi/kuWh9vav46j9TTZ4gaPBRdcDCB/7GU/7UvCy7/yx99TPK+A9nbmbHOvXG7Y9qBSCbvO82YNvUJKefJ7ENRDF6ym6XPdMDf4G433/bz1zl1K4FWVf1YfTr3OD/HUry88f7fdDLPnSXxzZFoqlTF8OoYUDQYp35fqGPoHn0CjX+AgyMt2LTlm5i0ZD1jn+5JSOWKzFq9lRc+m8yyT8fi7+tdYP609Aw+n78MPx8vJ0SbIzCsLg26dyV6zz60bm6Fzjd04SwC6tRk7pBXUc1men8+geFL5vN1+3AHRmtN5+tD31V/cOvvU/zZ51l8Q6vT4bMJKBoNOz60f+1y8/ai3+olxEaeZfmzQ3AvX54HJ76PoXIAS5x47VK8vCn34wwyo88S98YraKtUw2v0WFA0JE/+X6GOoX/sSTQVS/f8n7ZqG5OXb2ZMn3BCKvsze91OBn89k6UTRuJfiPM5LSODLxauooK3pxOizeLphccXkzFfiCLlvdfRBFbB/cXXQaMhbeZPdrMpBk/M1y6Tvn4F6q0baCoFoXt2GNra9Uh+6Vkwm5z3O2SZtu0Qk7ccZEx4K0L8fJm96wiDZ65k6at98PfysJknNcPE9G0R9G5ah6Htm6AoMH/PcQZMW8r8YY8RFuTv5N9ClFVlsuHSbOjzuOj1LOr3HOmJiURjaZQ8OP4tdn39PemJiTbzNX9xMK56PQsef5q0+AQAUm/H0u/3+VRu2pirBw858beAtIxMpm/ay9BOLenftgkAjYMD6TJxOvN3HmJUtzZ2877dq4PVdps61Ym8dotl+084vOFSvn9/NO7uXBgxAnNSEsmA1suLiqNGcXPKFMxJSfnmT4+KwnjIuWV9N+X+zuDqhnnuN5BmhMijmHV6NJ2fhK3LLWm2pKZg+nCI5RgPPAyl1HCpMGAAis6d6GHDMSclkbR9B1pPTyq9Pprrkwou/7SzUaRERDgpWjsxZJqYfiCSoc1r0b9RCACNK5ejy6yNzD9yjlEP1LWbd2zb+rylhAGwKeqaU+K9Q9e7L+h0JL09GjUlGfbtRvEw4DHkJVLn/mxJy4fi5Y3HiyNJmfRfPN+d4KSo80pLz2Daik0M7dmJ/l3aAtC4ZjCdX5/IvPU7Gd2nW4HHmLFqCwHlfKhasQJnLjvv/8OR5as5vMzS82DYojl4+hU85X9Iq5aEhXfmq/bhRG7fBUDc5au8vXczdTt14OTGLY4MOVvjIc/j4u7Okqcs167zmyzXrtbj3mTvN/avXU2GDcZFr+ePJ57JuXbFxvL44nkENG1MjJOuXfon+qHodMS/ORI1ORn27kYxGPAc9jIpv8ywpOVD8fLGc8QoEn/4Bp//fOyUmHNLy8hg+uqtDO3+IP07PQBA4xrV6PLml8zfuIdRj3cp8Bg/r9lOxXLeVPMvz5nLMY4OGQC3nk+i6HSkfDAGUpIxHfwLxcMT3cBhpC2cDXbqHtOJI5hOHMnZPnwA840YDF9MQhNaC3PkSafEf0daRibTtx1iaPsm9G9luYY2rhZAl6/mM3/PMUZ1aWkzn7urlrVvPIOPXped1io0iO7/XcC8Pcf45ImOTonfqe79WcVKRZnsYFfj4c5Erd9kVckfX/QHrh4eBLdrbTdfwH0NuXIwIrviBzi7YTOq2Uytbl0dGrMtEeeukJSaTnijnG4uHjpXOtQPZfvJ6CIfz9fDnQyT45+eeHXoQOK2bVY3yPHLl6PR6zHcf7/Dv/+f0tRpjHr6iFUDRT28C8VNhxJarxQjKxzvjg/mKf/YZZby92xV9ssfIOJqLEnpmYTXrJyd5uHqQoeQALafv55vXk0pVuZurdqS8dcuqwZK+obVKO56XJo2LzC/x7BXyDwSQca+PY4Ms0ARZ86RZEyl2/2NstM83HV0bFKf7UcKvpG5cjOWGSs3886zjzkyTJuKM+FMg25diL8Wk91oATi37wA3oqIJ61bwjWpJCXm4E9EbrK9df2ddu6rmc+2q2KgB1w4esrp2ncu6dtUId178ugfakb5np1UDJXWd5fx3bdqiwPyew18l/XAE6aV4/kdEXiDJmEZ4i4bZaR46Nzo0qsv2Y6cLzH/lVhwzVm/n3acL7p5YklxatCFz/26rBkrGlrWWuue+ZkU6lpoQD4Di6lqiMRZGxIUYktLSCW8Ymp3m4eZKh7rBbD9z0W4+rUZj1WgBcHPRUrNieW4npzosXnHvKZMNF7/atbh5+oxVWsLFy6QnJ+NXx/7bBhd3Hab0DKs0c2Ymqtmcbz5Hib5+G61GIdjf1yo9NKA80dcL130n02QmwZjK8gN/s/P0efo+0KjgTP+QLjSU9LNnrdIyrlzBnJKCrkaNAvMHffEFYZGR1PnrLyqNG4ei0xWYp0T5B8KNXF2M4m+hpqdaPivjdDVqkBaZt/xNhSz/al9/SaNzZwnbv5fA98ajuDu5/IHo2CS0/8fefYdHUa0PHP/O7mZLOpAQSICQUKSLUkQQFQWlWEAFUVB6FaQINvDq9apXLNgFKdIEQVQEqYoSBAHpvUPoLYH03SRb5vfHhoQlu5sQ2U383ffzPHlkZudM3hzPnpkzp4yiEBvuOtQivnwwiSnee4xKkzY2DvtJ14cKjosXUC1mtLHxHlLlpa1RG0OnzmR9/oEvQyyW4+cvodVoiK3kOrwiPjqKxHPeG44A781bQvvmt1K/ehVfhXhTVapTi4sHC9+UXjhwmEp1/Ff3l69diyuHXK9dGXnXrvK31PKYTmsw4sjNddl39dpV3o/x66rHYTtxffk/j2oxoyui/Otq1sb0cBcyP3nflyEWKfF8krPsR7n21MVHVyTxfFKR6d9bsIL2zRpS7wbmgt0MmqrVcZw+4bJPvXQB1WJBU6160SdQFNDp0FSJxdh/OPaDe7Ef3OuTWL1JTE513vdUcJ3LGB8ZTmJS6g2dK9dmZ9+5JGpUDC/6YPE/o0wOFTOWCyc7Na3Q/uyUNIzlPE/svXIskQZPPo5Gp8NhswFQ+fbGaHQ6jOXL+SxeT9IsOQTq9WivWzkizGTEkmsj12ZHr9N6TL/r5Dme+nQ+ADqNhnGP3UfbhjV9GjOANiwMe3p6of32tDS0oZ7Hxqu5uVyePZvMdetwZGQQ1KIFEYMHo4+N5dTAgb4M2ZUpCNXiplvdkoViCv57r2z1A52X/NeFey7/jtwckmbOIuOPdTgyMgm+swUVhw7GEBtLYr8Bvgy5kLScXAIDtGg1rr0nYYYALDY7uXYHem3Ze26ihIaiZhbOe0dGOkqI93khQS+8QvYP83GcOY2mUuk2kNOzLAQaC9c9oUEmLLm55Nps6HXuq/+/9h9l/Z5DrHj/ZX+EelMElgvH7OaaYU5JJSK+ut/iMJYLJyetcBw5qWkYwz3ffKUeP07dbq7Xrkp51y5TOf9duzyW//R0NF7qfoCQseMwL/wW+5lTaCqXXvlPM1sINLi57gYaseRavZf9g8f5c98Rlr/jn4UorqWEhKBmFh5KqGamowQXPSct8J3P0DVz9urZD+3HPG6498VofMR53xPg5r7HgMVa9H3Ptb5K2E66JYcnmpT9kRLCf4psuCiK0gYYDlwd73QA+FxV1QQfxuX+C6d42J9nx9ezuWPYINp/NIG1b00gsHx5On7yvvPJlY+HWKmqit1RENvV0S7uRr2oebfORY2IqVUpku9GPk26JYc/DiTy9o+/E2zQ0+l2z/MDbhq3+a94vem3JSVx/vXX87ez/voLW3Iy0W+9hbFuXbIPHLj5cd4QpVQq8pJwO1xGUbwOo7FdSuLsawX5n7lpE9bkZKq+8xamevWw7N/vi1CdZf+auBSUvHALF/Crh5Xpkb1us9572dG37YC2WhwZY4b5MDD3nHVPwapDV/NdcZPLBfnv/v+AzW7n7TmLGPxo22JN4C9T3Pz/Ker/m2/CuPHv7u6v59DkuUHcP/FdNrz9Hsby5Wj78Xs4bDYcfhgefK2SxG9o1wFtbBypo4f6MLLCPJZ9d9fdYpT9d+YtZfBD9xZrAr9PeMj74pTh7M8noISEoYmphr5HPwLf+ZysEX3Amltk2pLyeN/j9ljXY4qy9tBJvlq7gxc7tCAu8v9pj4vMcSkRrw0XRVE6AZ8DbwL/xlkebwe+VhRlWN76ze7SDQQGAnz11Vc3HFR2SipGN0+WjWGhZKcWfhp01eXDR1j63CgeeO9tmgzog8NuZ/v0WaiqStaloodH/B1bjp2h96SF+dvNalSh/a21ycrJxe5wuDx9SLfkYNLrCNB6f+oQaAigQdVKALSsHUuGJYcPl63zecPFnpbm9umaJiQEh5ueAG/SVqxwNlwaNPBfw8WSBUY3K5cYA1GzS2dZ7Bth89CzpQ0JwZ52Y/mfumy5s+HSsL7PGi5bzl6m96KN+dvNYirQvmY0Wbk27A7VpdclPdeKSacloAz2tgCo6ekowYVvWpQg909DAdDqCBz2ApZvpoNGgxIcghLkHCKnGE0QGAhms89i3nLwGL3emZS/3axODdrfcStZ2TmF6p4MswWTXk+AhyeeC9f8RYbZQufWzUjPcs4Rs9rtOBwO0rMsmAye05Ymc0oqwZGFl1o1hYe57YnxleyUVIxhha9dhtAQtz0xV105fIRVz43ivvfepnHetWv39NmggvlS0cObbhY1PR2Nm6f7SnAIqoeFBdDqCBkxBvOsaaA4y7/mavk3mVACA1F9VP63HEqk9/vT87eb3RJH+6YNyMp2d93NxqQP8Fh+v/9jKxnmbB5teTvp5oKyb3eopBfxvbkZ1IwMcFv3BKNmecj7azjOngZOYz+4F9ueHQR/8zMB93fAunKxD6J12pJ4nt5f/5y/3ax6Zdo3rEFWrrVw/mfnYAoo+r4HYM+ZS4xesJpuzerybMtGPold/HMV1eMyFuisququa/btVBRlK/AZ17105ipVVacAU65u/uf5GxtykHz4CBWuGw8cWiUGfXAwyYe8T67bNWsue+d/T4Wa8WQlJWNOvsyYc8fYMWPODcVwo+pXieK7kU/nbwcZ9FxMy8TuUDmVnEpcxfL5nyVeuuKyXVz1qlRk0ZZ9WO32Yn35Syrn+PFCcykCKldGGxREznVzX4p09TGLP596Jp1DiYx2fXAeVgHFYCw896UMyjl2DGPNm53/Nyk4N+pXDOe7bq3zt4P0Oi5mZmNXVU6lZRFXrmCeS2JKpst2WWM/mYg2Ns5ln6ZiJZTAQOwnj7tNo5hMaKMqETTiJYJGvOTyWchbH2A/c4rUrh19FnP96lVY+O+R+dtBJgMXr6Rhdzg4dTGZuMoF70A4fu4ScdGe34mQeOESF66k0XrYG4U+u2PweCYMfppHWt3YRGF/uHDwCHe5mfxeqU5tdv601G9xXDl8pNBclpAq0eiDgwvNfbne3tnzOLDgB8rVjMeclIwl+TLDzh5l90zfXruuZTuRiK76deU/qhKawEBsXst/ZUJGv0zIaNdrffg7E7GdPsXlx3yzJHX92Bi+e21I/naQ0cDFlHRn2b90mbhr5nglnk8irrLnJXUTLyRzISWNu0f/t9BnLYa/xbv9u/LInY1v7h9wDcfpE2irVnfZp0RGoZgCcZw6cUPnUi+dR81IQ1PZt/N06sdE8N2Qx/K3g/QBXEzPct73XE536SlJTEotVs/JieRUhsxZQYv4GMY95HnlVfG/q6iGS6XrGi0AqKq6W1EUny3UfmzVau4cNRx9cDC5eSsr1XuiC1azmZPXrBrjiT0nh0v7nE/3G/XsjqLRsP+Hn3wVLgBBRn1+78hV0eVCCTbqWbXrMIPbtQDAkmslYf9xura48acIOxLPUSks2KeNFoCMhAQiBg5EExSEI291mdCHHsJhsZD11183dK7QDs5lVy17/TdJ0HFoJ5q7Hwa9EXKdq5Eoje5Ezc1BPV7aw9WKlr5mLRUHu+Z/+CPO/M/cdGP5H97JecNs3r3npsd5VZBeR4Mo1wtSdIiJYL2OVUfPMbiZc3KxxWojIfEiXevH+iyWvyt303pMPfq49JLo27ZHzbZg277VbRrVYiZtaB+XfZoKEYT8532yJn2MbeuN/T+7UUEmIw3iq7rsi65QjmCTkZV/7WJIZ+eqVJacXBJ27KdrmxYez9Wj3V20beK6DPjUn3/nTNIV/t33CeKjS//9NO7sXfErnf71EjVateDYn84Vrao1uY3IGnHsW/Gr3+JIXPUbzUYNIyA4GGvetatO3rXrdDGvXcl51676PZzXrkM+vnZdK2fjOoJ69nXpJTG264CabcG6fYvbNKrFzJVBvVz2aSpEEP7Oh2R88RG5W3xX/oNMBhpct4BEdIVwgk0GVm3Zy+CHnUvoWnJySdh1kK53e14Z7en7WnD/ba5zKaat+IMzSSm88eyjxFf27UsQbVv+xND1WTAFgsWZ9wH3PuCse3ZvKyK1K02VWDRh5XCcP+uLUPMFGfQ0uO79KtHhwQQb9Kzae5zBbW4H8u57Dp2ka1Pvc1WSMrIYMHM5VcuH8kG3+wvNk/l/R4aKlUhRDRdv42p8NuZm29QZNBs6kK4LZrPhw08Ij6vOPeNfZNOnX7osM/ncvq2cXLeBpYOdb2XXh4TQ+qXRnFy/AYfNTvV77qLFyOdYOnQk2Sk3tprFzWAI0NH/vuZM/nUToSaj8wWUf2zD4VDpcVfBk5vFW/czfsEqVr7Sj5jyoZy9ks74BavodFsdqlQIw5xjZfXeoyzfeYh/PX6/z+O+MncuFXr3ptrkySRNnoy+WjXnO1ymT3dZorfWmjWY//qLsy87n7JVHDECTXAw5q1bsWdmEtS8OREDB5K2YgU5B/23lrz612po2R7NM6NR1y6B8lFo2j6Bun6ZyxLJ2jEfoyYewPFDwXBGpXZj0BtQop0310oD5/LD6pljkOqftxBf/uYbIvv0Jm7KZC5OmoyhWjUqjRrJpanTXPK/7roEMjf9xemxzqf8lUaNRBMcRNYWZ/4H39GcioMHkbp8Bdl+zH8Ag05L/yY1mbzlMKGGAOcLKHccx6Gq+e91AVh84DTjf9vFymfvIybUObzvbLqZvZec31erXeXYlQxWHT2HSafl7uq+vXHOWfQdpq49CPnvJ2R/Mx1NdBUC+w3F8u1slyWSwxcux7pjK1nv/Avsdmw7XG/qrk7Otx87gm2/7xqNnhj0AQx46D4mLf6V0CAT8dEVmbniDxyqg54P3JV/3E/rtzJ+6gJWffgKMRHliY2KIDbKdcjVoj+2kJKZRfO6vl8YBCDAZKJhR+fy9eEx0RhDQ7j98UcB2LP8F6wWC28e2cmRteuZ0985pyhx02b2rVxN79lf8cOY8fkvoDyyboPf3uECsHPaDG4fOoDO82ex+cNPCYuLpeW4F9n66SSXa1f/vVs4vW4Dq4aMAJzXrhYvjebM+g04bDaq3dOapiOGsmroKL9euyw/LCDwyZ6Evfcp5tnT0cZUIWjAc2TNneWyRHKFH1di3b6F9LdeA7u9UKPm6uR829HD2Pbtxp8MAQH073APk5euITTIRFylSGb9st5Z9+S91wVg8YYdjJ/xIyv/O5qYiHLERlUotBLZT39uJyUji+Z1vK+odjPk/vw9+s7dCXzjA3IWzERTuQqGZweR+8NclyWSg2ctxrZ7G9kfOt8TZRg4Ehx27Af2omZmoImNw9CtF46zp7EmrPJ53NczBOjof3djJidsJ9SkJy4ynFl/7sGhQo87Cx6KLN5xmPGLElg56iliyoWQbbUxcNYK0rNzGP9wKw5fLFh5NUCrpV504aGg4n9TUQ2XGoqiLHGzXwF89k3OTk3jmw5daP/xBJ78YR7ZqWn89dlk1v7nXZfjNDodmmvGyqt2O1GNG3Fb32fRmYwk7TvAD0/34dDPbke0+cWA+5rhUFWm/r6Z1CwL9atWYtqgJ4gICco/xuG4OsHNOZ4n1GQgMjSIyav/IjkjixCTgZpRFZjUvzP31PV9BepIT+dEjx5U/ve/iZ02DXt6Ope//ppLH3/scpyi08E1vT85x44RMWAA5bp1QzEasZ47R/KUKSR98YXPY3ZhycI+7S00j/ZB0+tFsGShrl+OY/VC1+O0WrjuiY6mSz+UcgVPkLQ9navL2BdOQt221uehA9jT0jn61NNU+c+bxM+Yjj0tnaRp07kw8br81+pQrsn/7GPHqDhoABW6P5mf/5cmf8XFz/yc/3kGNKnpLPtbj5KanUv9iuFM63wnEYEFyzM7wGViP8Dms5cZt7rghXurjp5n1dHzRIeYWN3btw0XNSOd9OH9CHphHCHvf46akYFlwWws0657c7WbslPWDHj4Pmf+//w7qZlZNIiryvSXBhFxzcRj1eFwTm4uQ2tWhFaMZOD3rsOjrm6Pq96AyydPodVp0VzX8zytex+6fvRfnv36CxSNhj1LV7Hg+bF+ixucq4d91/Ex7v9oAl1+mEtOahpbP5vMhrcmuBznvHYVxK/a7UTd2pBGfZ5BZzKSvP8gS3r05aifr11qRjopQ/sSOnYc4R9+gSMzA/O3s8ia4lqHKFotlNF5agADOt6NQ3UwdflaUjPN1K8ew7TRfYgIKxim6rzuOrycxc8yMzCPHYxx+MsE/udj1MwMcn+YS87s6+YJX1f32A/vR9+5O/qOj4Fej+PSBazrfiP32xmQXTrvPxlwd2Nn3fPHTlLN2dSPiWRa705EBBfMPXVcN7H/cqaFQxcuAzBkzkqX80WHB7N6TA//BC/KPMXbSiGKotzjLbGqqsW5k1P/Y7zx+RxlwWvZzha/femNLzBQFmgfGgTA3ri4Io4smxokJmJ7uXtph1Fiunfns/O6Mcv/JI1Pn8D++ZjSDqNEtMOc71K5fGeDIo4smypsdA6tdGz23/yMm0nT/CEABiv/sJXJ8kxW03nfVKHoA8uosZbLXGxWr7TDKLGoLfuxr/++tMMoEe1dTwCQ3vb2Uo6kZEJXbwfAvnBiKUdSMtquo6GML1x5lf29oWXokVFh2he/LJP56LXHxVPDRFGUqkB3wD+PoIUQQgghhBD/04rd16soSoSiKEMURfkDSADK5ixNIYQQQgghxP87Rb3HJQToAjwN1AYWAfGqqlbxlk4IIYQQQgjhgawqViJFTc6/BGwGxgPrVVVVFUXp4vuwhBBCCCGEEKJAUUPFXgWMwCTgFUVRahRxvBBCCCGEEELcdF4bLqqqfqSq6h3AIzhXafgJiFYU5SVFUWr7I0AhhBBCCCH+X1HPTM5OAAAgAElEQVSUsv1TRhVrcr6qqsdVVX1bVdWGQDMgHFjh08iEEEIIIYQQIk9Rc1wKUVV1j6IorwH7fBCPEEIIIYQQQhTitcdFUZRQRVFeURTlc0VRHlCchgNHga7+CVEIIYQQQgjxv66oHpc5QAqwEegPjAX0QGdVVXf6ODYhhBBCCCH+/9EU+1WK4hpFNVzi8+a1oCjKNCAZqKaqaobPIxNCCCGEEEKIPEU196xX/6Gqqh1IlEaLEEIIIYQQwt+K6nG5VVGU9Lx/K4Apb1sBVFVVQ30anRBCCCGEEP/flOElh8syrw0XVVW1/gpECCGEEEIIITyRmUFCCCGEEEKIMu+G3+MihBBCCCGE+BtkqFiJSI+LEEIIIYQQosxTVFX19e/w+S8QQgghhBAC5wJSZZ7945Fl+v5YO/LjMpmPMlRMCCGEEEIIf5KhYiXil4bLVyER/vg1N92gjGQA7MumlHIkJaPtNBCAlRHRpRxJybRPPof9jd6lHUaJad+YybqoKqUdRom1vngG+8KJpR1GiWi7jgbg9K11SjmSkqm66yAAjj0JpRtICWka3gvA+6YKpRtICY21XGaw8s9d7X+yms6l5vVKO4wSq7h5P44Ni0o7jBLRtOwCwNnb/pl1T8wOZ91jX7ewlCMpGW3rrqUdgvAxmeMihBBCCCGEKPNkqJgQQgghhBD+pJG+g5KQXBNCCCGEEEKUedJwEUIIIYQQQpR50nARQgghhBBClHkyx0UIIYQQQgh/kuWQS0R6XIQQQgghhBBlnjRchBBCCCGEEGWeDBUTQgghhBDCn2SoWIlIj4sQQgghhBCizJOGixBCCCGEEKLMk6FiQgghhBBC+JMifQclIbkmhBBCCCGEKPOk4SKEEEIIIYQo82SomBBCCCGEEP6kkVXFSqLMNlzCb6nNXR+8S8XmTclNS+fgrG/Y9t/3UB0Or+kibmtM89fHEXnbrYBC8q7dbHnzbS5t3e6fwK+jqipTftvMgg07ScnMpkG1KF7tch91Yyp6TffZyj9Zvfso51LSUVGJiyxP3zZN6XBbHb/EHVS7FvXefYvwpk2xpqdx5ptvOfreh+Al/2u++AI1X3zB7WeH//MOxz/53FfhFhYZjaZDT6haA7LNqNv/QE34CVTVezqDCaX90yh1bgdFQT28C3XFN2DJ8k/ceQJr16LGO/8hpEkTbOlpXJw7n5MfTPSa/1dV6NiBqs8/R2CdOjgsFjJ27uJAvwE4zBY/RF5AVVWmrN3Bgs37STFn0yAmklcfakXdyhEe09gdDmas303CoZMcu5QCQP3oSEa0a0bDKt6/MzeLLr4G5V4ej75RY9SMDDIXLSR98hfFynsAFIWoed+jr1efpOGDyf4jwafxeqKqKlN+XMH8X/4gJSOTBjViGde3O3Xjqhb7HKs372T4e5OoH1+N798b58NoC1Socwv3T3yX6DuakpOWzu4Zc9jwdtF1f4W6t3Dfe28T0/IOrGYLh39cQsKrr2PN8t93N7JGPO3GPk98i2ZEN6jH0XUbmNimU5HpjKGhdPv4XW7t3AmNRsOepatY8PyLZF254oeoXWnjahAyZhwBDW/FkZFB9uLvyZr25Q2V/3IzvyOgbn1SRw8hd/1a3wbshqqqTFmWwPzfN5GSmUWDuCqM6/EIdatFF/scq7fvY/hnc6hfPYbvXx/uw2gL6OJrEPZiXt2TmUHWooVkfHVjdU/kN8665/Lzg8lel+DTeD1RVZUpy9eyIGEzKZlmGlSP4dWnHqJutcpe0322+DdWb9/PucupqKpKXKUI+j7Ymg7NG/opcvFPUCaHiunDw3jo5x9RVZVV3Z9h27sf0Gj4EJqOe8lruqCYaB5a8gManY41A59jzcChaHQ6Ov70PcFVq/gpeldTf9vM5F820a9Nc77o35lAvZ5+kxeSlO79YpqVnUvn5vX58NmH+KT3I9SrUpEX5ixj1a7DPo9ZFxZGsx8XoKqw/Zk+HPvgI6oPGUStl8Z4TXd6zjw2PviQy8/VxkrSb2t8Hnc+YyCaZ8cCKo5vP0VduwTlzvYobboUmVTTdShK9To4lszA8dM0lJg4NN2f93nI19KFhdFg4beoqsr+Xn059eHHxAwZSKyHRuG1ono8RZ1Jn5HyewL7nn6GI6PHkp2YiKL1/zOKqX/sZHLCdvrd3ZgverYn0BBAvxnLSMowe0yTbbUz7Y8dNIyJ5N0n7mNC1/vQaTX0nLqYfWeTfB6zEhJKxa9mgKqSPPI50qZ8QcizfQgdUvwbl6DHuqKt6J9GljdTF61k0g/L6d/5Qb58+TkCjUb6vvkRSSlpxUqfk2tlwsyFRISH+jjSAobwMLot/xFUlUVdn2HDO+/TdMRQWr32std0+tAQnlzxEzqTkZ+f6U/CK69Tq/NDdJox2U+RO0XXr0ODjg9w8fBRLh4+Wux0AxbMpPa9d/FN/+HM6j2E2Ga3M/ineT6M1D0lJJTwz6eDqpI6ZhhZ0ycR2KM3QQOHFfscxkefQFMxyodRFm3qsgQmLfmN/h3v4csRvQg0GOj7/jSS0jKKlT7HamXC/GVEhAb7ONICSkgoEZNnACpXRj1H+pQvCH6mDyGDi1/3BHYpI3XPij+YvDSBfh3u5ovhPQk0GOg3cUaR+Z9lyaFzy9v4cNCTfDL0KerFRvPClAWs2rrXT5GLf4Iy2eNSr19vdEYjv/TohTUjk7Nr1qIPDaHJK2PZ+fFnWDMy3aar9uADBIQE80uPXuSmpQNw4a/N9DpxmGoPtGP/9Bl+/Csgx2pj2u+bGXB/c3q0vg2AxrHRtHtrKvPW72BEx7s8pn25cxuX7Va3VOfohcss2bqPB2+t7dO4q/V+Bq3RyI5e/bBnZnJ5LehCQqg59gWOf/Yl9kz3+Z9z/jw558+77KsxZiSZh4+QsXefT2O+ltK0Dej0OBZ8BjnZqMf3gcGIcm9n1D+XQ062+4RVaqDUbIh9xjtw0tlAdGSkoB3wOsTXg+P7/RJ/pV490RiNHOgzwJnXf6xDFxJCtTGjOfP5JI/5rytfjvg3X+fYuH9x4ZuCm57LK1b6Je5r5VhtTPtjJwPuvo0eLRoA0LhaFO0+mMe8TXsZ0a6523TGAC2rXniaMJMhf1+L+Bg6fjyfuZv28s7jbdymu1mCu3ZHMRpIHj0cNSuLnE2gCQomdPAwMmZOQy3i6b0SEkrYsJGkffoh5d9426exepOTa2XqTysZ0KU9PTo486xx7XjaDn2VuSvXMPKpzkWeY/riX4gqH07VSpEcOXXW1yED0Lh/H3RGIz9170VuRgYnfwdDaAgtx73I5omfkZvh/sbntoH90JlM/Pj40+Tk1f3ZKSk89v1com5vzMXtO/0S/+6fV7BryXIABi6cTXBEhSLTxLVoTv32bfng7vYcXbcBgNSz53l58xrq3H8vB39L8GXILkyPPYliMJD20vOoWVlYN29EExRE0IDnMM+ZXqzyHzxkBJlfTCR0/Ft+itpVjtXK1OUJDOh0Lz3atgSgcc1Y2o6ZwNzVGxj5+INFnmP6ij+ICg+lasXyHDl70dchAxDUtTuKwcCVF5x1D385656QQcPInFW8uid02EjSP/2Qcq+XYt1jtTJtxR8M6Hg3Pe5rAUDjGtVo99IHzPt9EyO6tPOY9uXuHV22W9WvxdGzl1iycScPNm3g07hLhawqViJlMteqtWvL6d/WuDRQjn3/IwGBgUTf1cpjOk2ADtVmw5pZ8AW3ZWah2mxQCkMJd5w4R2Z2Lu0bFzQ0Ag0B3Fs/nnUHE2/4fOFBRqy2YnYZ/w0Rbe8jec1alxvk8z8uRhtoonyrO4t9noDwcCLuuZvzP/7kizA9Umo1Qj2216WBou79CyXAALGeh9optRqhZqblN1oAOJuImnIJpWYjX4bsovx9bUhNcM3/pJ+c+R/WsoXHdJGPPAzAxQULfR5jUXacukhmTi7tG8bn7wvUB3BvnVjWHTntMZ1Wo3FptADodVpqVizPlSwPDc6byHhXa7I3rHe5STCvXI7GZMLQ1H1j61phw0aQs3M72X9t8mWYRdpx6BiZ5mw6tGyavy/QaKBNk0as21H0Q4RzSVeYvngVr/R90pdhFhL34P0krv7dpYFyYKGz7q/auqXHdBVvbcCF7TvzGy0AJ1avQXU4qNHe843SzaYWNRTVjQYd2pF24WJ+owXgxJZtJB1PpH4H/8UOoG/ZmtxNf7qU/+xfVqAYTQTc1qzI9EGDhmPdvYPcLaVX/nccOUmmJYcOzQvq7ECDnjaN67BuT9EjFs5dTmX6irW88vTDvgyzEGOr1mRvdK17LKvy6p4mRdc9oUNHkLtzOzmlXfccPUWmJYf2TQuGdwUa9Nx7ax3W7Tlyw+cLDw7EarPfzBDFP5zHhouiKB77SBVFqeGbcJzCa9ck9bBrAc88cxZrVhbhtWt5TJe4eCk2s4U733kTY0QExogI7nz3LXJSUzn+0xJfhuw+nktX0GoUYiPLueyPr1iBxEvFG7tssztIt2Tz87YD/HnoJN1a+v4GOqhmTbKOuA5zyD57FluWmaBaNYt9nqhHOqHR6zm/aPHNDtG7iMqQ7NrzQ9oV1NwclAgvY2zdpQNIOu893U1mqlUT85FjLvtyzp7DbjZjquk5/0Nuvw3L0WNUero7zXdsodWZRG5d8TMhTZv4OuRCEpNTnWW/QpjL/vjIcBKTUm/oXLk2O/vOJVGjYvjNDNGtgLh4rImuDxXsF87jsJgJqB7nPW2t2gQ92oW0ie/5MsRiOX72AlqNhthKrsNG4qtUIvHshSLTvzdrIe1bNqV+fDVfhehW+dq1uHLIte7POH2W3Kwsyt/iue7XGow4cnNd9jlsNlSHg/J1fNtD/XdVqlOLiwcL31BfOHCYSn6OXRcbh/2ka/l3XDyPajGjqx7vIZWTtmZtTA93IfOT930ZYpGOX0hylv0o17l08dEVSTx/qcj0781fRvtmjahfPcZXIbqlqx6PzUPdoyui7tHVqk3go11I+6j0657EC8l5+e/a2xhfOZLEC8Ub7muz20k3W/h5007+3HeUbvcW3WgW/zu8DRXbpSjKK6qqfnd1h6IoRmA88CTg+SryN+nDw8lNKzwOOyc1DUN4mJsUTuYLF/i5U2faL5xHw6GDAMg6f4FlnbuRnXzZV+F6lGbOJlCvR6txbR+GBRqw5NrItdnR67Qe0+86cY6nPv0WAJ1Gw7jH7qNtQ59le76A8DCsbvLflpZKQJjn/L9e5S6PkrZrN+Zjx29meEUzBkK2m3kU2VlgCvSYTPGQTs3OQikXeTMj9EoXFoYt3U3+p6ah81L+9RUjMdWsQdVRI0j8z9vYrqRQZdgQGsz/hq13tsaalOzLsF2kWXII1AcULvsmAxZr0WX/Wl8lbCfdksMTTer6IlQXmpBQHBnphfY70tPRhHov++Evv0bm/HnYTp9CG+3fm57rpWeZCTQa0Gpd8z80KAhLTi65Vhv6APfV/197D7F+135WfPqmP0J1YSwXTo6Hut8Y7rnhmnr8OHW7PY5Gp8NhswFQ6fbGaHQ6TOXKeUxXFgSWC8ecWvhvNqekEhFf3a+xKKGey78S4n2uU8iYcZi//xb7mVNoKhd/EvzNlp5lIdBQ+LobGmjCkmsl12ZDr/NQ9g8cY/3ew6x41/t8Tl/wVPeoxal7XnqNrAXzsJ8+hbZy6dY9aR7yP6wY+Q+w69hpnvrvVwDotBrGPf0QbW+r59OYS40iq4qVhLehYg8AfRRF+VVRlJqKojwK7AEMwG2+Dsxdl7uiKF674gOjomg352uSd+5ieZduLO/SjeSdu+jw/bcEV/Htl1lVVWx2R/6PPW8VEHfl8uqfUFSZrVU5ku9G9WDa4Cd4+q7GvP3j7yzbfuAmR+6Bu3xWlKJX5cpjiKpI+ZZ3+n2YWD63cSpQVPglTXezuQ2jiPzXaNAFB3Nk1BiSflhEypoE9vfqh2q3E923t68i9Vz23R7r/G9x6+u1h07y1dodvPDgHcRF+r7HBShR2Te170hA9eqkT53kw8Dcc+a/Pf/Hbr9a9xTO5Kv1p7vPwPmk8+2v5zP48Y5Eliv+Q4qbyW0dX0Tdv/vrOQRGRnD/xHcJiqpIhbq30Pbj93DYbDjs/4BhJh6ud8Wtb30di/ML6zkWQ7sOaGPjMH/t38UQCpV9b9fdvP8qHsaN2+x23p67hMEP30dkWIiPIi5CSeqeBzuii61OxrQyUPd4zf+8uqeIcfu1qkTx3fghTBvdm6fbtODteUtZ9teumx67+Ofy2OxVVfUY0EFRlLHAQeAC8KCqqkUOkFYUZSAwEOCrr7664aByU1MxuHmyrw8NyZ90786tI4eh0en4tWef/KduZ9euo/vOzTR6/jk2vPjqDcdSXFuOnaH3l/mdUzSrUYX2jW8hKycXu8Ph8vQh3ZKDSa8jQOv9iXOgIYAGVSsB0LJ2LBnZOXy4dB2dbvftk2draho6N/mvCwnFmu45/69V6dGHQVG4sMj/Q/TINjt7Xa5nNLnvicmjZptRAgtfsDz1xPiKLS0NXWjhp5u60BBsXsq/LdU5BCt1w8b8ffbMTDJ37yGwtu+GnGxJPE/vr3/O325WvTLtG9YgK9dauOxn52AKKLrsA+w5c4nRC1bTrVldnvXDEEkAR0Y6GjdPljXBwW6fhgKg0xE+aizpM6aBRoMSEoImKAgAxWRCCQxCNftuSd4t+w7T642J+dvN6tWmfcsmZFmysdsdLr0uGWYzJoOeAA+9XQtXrycjy0Lne+8kPctZ5q02Gw6HSnqWGZPB4DHtzZCdkorRTd1jCA1x2xNz1ZXDR1j13Cjue+9tGg/og8NuZ/f02aCC+ZLvV6P7O8wpqQRHFl4i3BQe5rYnxpdUDz0rSnAIqoeFEdDqCH5+DObZ00DRoASHoAQ5R5orRhNKYCCq2Tf155ZDx+k1YWr+drNb4mjfvBFZ2YWvuxlmCyZ9gOeyv3YLGeZsOre6nfS8peOtNjsOh4N0swWT3vP35mbwVPcoRdQ9oSPHkjHzmrwP9mPdcyiR3h98nb/drHZ12jdr6Db/083ZXvP/qkCDngZ5w/Ra1qtJhiWbD3/4hU533OqbP0L843hsuCiKogPGAv2AoUBH4FNFUYaqqnrI20lVVZ0CTLm6+dULN9ZgSD18tNBclqCYaAKCgwvNfblWeK2aXDl4KL/RAuCwWkk5cJDQeO9jRP+u+lWi+G5Uj4J4DXoupmVid6icSk4lrmL5/M8SL11x2S6uejFRLNq8D6vdXqwbv5LKOnqU4Ovmshijo9EFBxWa++JJ5S6PkvLXZrLPnfNFiN4ln3fOV7lWaHkUvRGHuzks16a73c0NfkRl1IP+ew+Q5chRTLVcp5HpoyujDQrCctRz/psPH0V1OJw9k9fsVxSlyHdg/B31YyL4bshj+dtB+gAupmc5y/7ldJeeksSk1GL1nJxITmXInBW0iI9h3EOeF+S42ayJx9HFuY7l10ZVQhMYhPWE+wU1FJMJXaXKlBv7CuXGvuLyWcR7H2E9dZILDxe9klFJ1a8Ry8J3C35vkMnIxSup2B0OTl24RFxMpfzPjp+96LJ9vcRzF7hwOYXW/ccW+uyOXqOY8HwfHrnb8wIRf9eVw0cKzWUJqRKNPji40NyX6+2dPY8DC36gXM14zEnJWJIvM+zsUXbPnOOzeG+GCwePcJebhQcq1anNzp+W+jUW28lEdLGu10pNxUpoAgOxnXA/5FcxmdBGVSZk1MuEjHJdtjrsnYnYTp/iyuPtfRJv/dgqLPxXwVLNQUYDF1PSnGX/4mXiKhcM8T1+Pom4yp6XCk68kMSFlDRajyy8Itcdz/2bCQOe5JGWvhtsYjvhue6xeap7jM66J3zMK4SPca17yk/4CNupk1x81Id1T/UYvhs/JH87yKjnYkq6M/8vXSauUkH+J15Ictkurnqx0Sz6cztWm92nDUfxz+FtjssOYC3QRFXVNGCKoigPAYsVRVmkquorXtL+Lad+Xc2tzw8jIDgYa97KSjUe74LVbObc+j89pss4fYZqD7RFExCAw2oFQKPXU65eXU6tWOWrcAHnF/Zq78hV0eVCCTbqWbXrMIPbOS/2llwrCfuP0bXFjT9B3nHiLJXCg33aaAFIXv071YcNQRschD1vhbZKXR7BbrZw5c+NRaQGU9UqhDdryr6x3t+94Cvqkd0orTqg6o2Q61yJSmnQHNWaAycPek2nuedRqFYLTuXdJEVXRylfEcfR3f4IHYArv6+hytAhaIOCsOetMBP5qDP/0zZ4XjHmyq+riR07mrBWLUn57XcAtCEhBDdqyJlJN97zWVxBBj0NYlwvSNHhwQQb9Kzae5zBbW4H8sr+oZN0beq9xzApI4sBM5dTtXwoH3S7v9BYaV/KXr+OkN59XZ5UBj7YEYfFQs7WzW7TqGYzl/o967JPExFBxISJpH4ykRwfr7AUZDLSoGZ1l33RkRUIDjSycuM2hjzhfAGiJSeXhK276drO8zLsPdq3oW2zxi77pv60kjOXLvPvgT2Ir+LbRSoSV/1Gs1GudX+dJ5x1/+lrVt3yxJ6TQ/I+53Da+j26o2g0HPqhlIarFtPeFb/S6V8vUaNVC4796Swr1ZrcRmSNOPat+NWvseRuWEdgz74uvSSGdh1Qsy1Yd2xxm0a1mEkZ3Mtln6ZCBGFvf0jmFx+Ru/Uvn8UbZDLQIM71HW3REeEEmwys3LKbIY/cD+SV/Z0H6HqP59W5etx/J21vd51LMXXZWs4kX+HfvboQ76XRczNk/7mOkGdd6x7TA3l1zzYPdY/FTFJ/17pHGxFB+XcnkvbZRHI2+7juMRrye0euiq7gzP9VW/cy+CHnUuyWnFwSdh2i691N3Z3Gqx1HT1KpXOj/z0aLH69t/594a7j0VlV127U7VFVdqijKapwT9H1m//SZNBg8kAfmzmTnR58SGledpq+MZc/nk1yWSO6+czPn/9zA2udGAnBw1jfU6dWTB76dzf6pX4OiUH9AXwIrRbF/xmxfhuyWIUBH//uaM/nXTYSaDMRVLM+stdtwqGr+e10AFm/Zx/gFq1j5an9iyody9ko64+evpNPtdahSIRxzTi6r9xxl+Y5D/OuJtj6P+9TMOVQb2I/bZk4n8dMvMFWvRs2xL3Bi0lcuS/S23vwnKRs2sXek64sRK3XpjMNq5cIS/z4tvErdugbljnZonhyG48/lKOUine9w2bjKZYlkzfMTUE8cQl2S19V95hjq0T1ougzA8csCUFU0bbuinjzkt3e4AFyY9Q0x/ftSd8ZUznz+JcbYWGLHjubsV1Nc8r/ppvWkbdzEkVHOiaSZu3aTvGIltT96n8S3/ov1yhWqPjcU1Wrj/Nez/BY/5JX9uxszOWE7oSY9cZHhzPpzDw4VetxZsB7/4h2HGb8ogZWjniKmXAjZVhsDZ60gPTuH8Q+34vDFgtX3ArRa6kUXHlJzM2UunE/I0z2JmPgp6TOmoatSldAhz5ExZ6bLMqWVfl5FzrYtpLwxHuz2Qo2aq5PzrUcPk7vHf43eqwz6AAZ0bs+k75cRGhRIfEwlZi5djUN10LPDffnH/ZSwkfFfzmbVF28RE1mB2MoVib3uBm1RwkZS0jNp3uAWn8e9c9oMbh86gM7zZ7H5w08Ji4ul5bgX2frpJJclkvvv3cLpdRtYNWQEAPqQEFq8NJoz6zfgsNmodk9rmo4Yyqqho8hOubFV7P6OAJOJhh0fACA8JhpjaAi3P/4oAHuW/4LVYuHNIzs5snY9c/o7ewoSN21m38rV9J79FT+MGY/qcNBlwpscWbfBr+9wAbD8uADTkz0Jm/ApWbOno42p4nyHy7xZLuW//A8rse7YQsZbr4HdjnW7a6Pm6uR827HD2Pb5t/wbAgIY0PFeJv38u7PsV45k5qp1OFSVnm0LerZ++nMb47/+gVUTxhITUY7YqIhCK5EtWr+NlMwsmtfx6UKqAGQtnE9w956U//BTMmdOQxtTlZDBz5H5jWvdE7V4FTnbt5D6b2fdk3tdo+bq5HzbkcNY95ZC3RMQQP8OdzN5aQKhgSbiKkcy65c/nfc99xW8TmHxhh2Mn7mIlf8dRUyFcpy9nML4GT/SqfmtVIks57zv2b6f5Zv38K+ej/j97xBll7c5Lts8fNQE8OkyLbmpaSx9qAt3fTiB9t/NJSctnd1fTGbbO65L/Wl0OpRreh+Sd+5iRZduNHnlRdpM+RKAK/sPsOyRJ7jixxcgXmvA/c1xqCpTf9tMalY29atGMW3QE0SEBOUf41BV7A6Vq9MHQ00GIkODmfzrXyRnZBFiMlAzqgKT+nfhnnrel6S8GWxpaWzp0o16E97m9rkzsaanc2LyFI6+96HLcc78L/zEoHKXR7n8x3qsl4u35PNNl23GMfs9NB17onlqJGSbUTf+gpqwyPU4jbbQEw/H95NQHnwKzaP9nBOCD+9EXTHXj8E783/PE92p8d+3qDd7Jvb0NM5+NZWT7090OU7RalGui//Q0OeJf3088f9+HY3JRPqWLex+4klsXuYH+MqAuxs7y/4fO0k1Z1M/JpJpvTsREVww/6ig7DtdzrRw6IJzBcAhc1xfnBkdHszqMT3wJTUjnUsD+1DuldeI+HQSakY6Gd/MIn3S5y7HKVodiqZsPwEc0KW9M/8XrSQ1M4sG8bFMf20kEeEF4+hVVXVOqC2NSeBu5KSm8V3Hx7j/owl0+WEuOalpbP1sMhvemuBynEanQ3NN3a/a7UTd2pBGfZ5BZzKSvP8gS3r05ejPy/0af2jFSAZ+7zo07er2uOoNuHzyFFqd1iV2gGnd+9D1o//y7NdfoGg07Fm6igXPFx6u52tqRjqpz/UlZOw4wj/8AkdmBpZvZ5E19QuX4xRt4bqzLBnQ6V5n2V+2htRMMw2qV2H6mH5EXDPpPr/s+33lFffUjHSSB/ch/KXXqPDxJBwZ6WTOnUXGZNe6B53Oee0qwxLx9gMAACAASURBVAZ0uBuHQ2Xqij9IzTRTv3oM00b3JiKs4C0bjvy6x7kdajIRGRbK5GUJJKdlEhJopGZ0JJOef4Z7Gvn+oYn451CK88IsRVEaA08D3YBE4AdVVT/3niqf+lWIb5+S+sqgDOfysfZlU4o4smzSdhoIwMqI0lua8u9on3wO+xu9SzuMEtO+MZN1UVWKPrCMan3xDPaFE4s+sAzSdh0NwOlbPb9wtCyruss5pNGxJ6F0AykhTcN7AXjfVPSb48uisZbLDFa8L/9blk1W07nU/J+7hGzFzftxbFhU9IFlkKZlFwDO3vbPrHtidjjrHvu60n+RcUloW3eFUnnl+I2zT/9X2Wg1e6Dt92aZzEdvk/NrA92Bp4DLwAKcDZ02fopNCCGEEEIIIQDvc1wOAuuAh1VVPQqgKMoov0QlhBBCCCGEENfw1nB5HGePyxpFUVYC8/mHdL8JIYQQQghRZilld55YWeYt135WVfVJoA6QAIwCohRFmaQoygP+CE4IIYQQQgghwHvDZTOAqqpZqqrOVVX1IaAKsBMonRd0CCGEEEIIIf4neRsqVmhYmKqqV4Cv8n6EEEIIIYQQN0qR2Rcl4a3hEqkoymhPH6qq+s9cJ1UIIYQQQgjxj+Ot4aIFgpEJ+UIIIYQQQohS5q3hcl5V1Tf9FokQQgghhBD/CzSyqlhJeMs16WkRQgghhBBClAneGi73+y0KIYQQQgghhPDCY8MlbwUxIYQQQgghhCh13ua4CCGEEEIIIW42WQ65RGRmkBBCCCGEEKLMk4aLEEIIIYQQosyToWJCCCGEEEL4kyJ9ByUhuSaEEEIIIYQo8xRVVX39O3z+C4QQQgghhOAf8h5C+5x3yvT9sfaZV8tkPvplqFham8b++DU3XdianQA4Niwq5UhKRtOyCwDp991WypGUTOjvO7B/Mba0wygx7XPvYxvxaGmHUWK6TxZjT5hf2mGUiPbe7gBkP3NfKUdSMsY5vwNg//qN0g2khLR93wDgYrN6pRtICUVt2c+l5v/M2AEqbt7PYCW0tMMosclqOvbPXijtMEpEO/xDANLbNSnlSEom9NdtADj2/1nKkZSMpl6r0g6h+DRlsl1Q5slQMSGEEEIIIUSZJw0XIYQQQgghRJknq4oJIYQQQgjhT7KqWIlIrgkhhBBCCCHKPGm4CCGEEEIIIco8abgIIYQQQgjhT4pStn/+9p+nlFcU5VdFUY7k/becm2MaK4qyUVGUfYqi7FYU5cmizisNFyGEEEIIIcTN9DLwm6qqtYDf8ravZwaeVVW1PtAe+FhRlHBvJ5WGixBCCCGEEOJmehSYlffvWUDn6w9QVfWwqqpH8v59DrgERHo7qawqJoQQQgghhD+V8VXFFEUZCAy8ZtcUVVWn3MApolRVPQ+gqup5RVEqFvH7mgN64Ji346ThIoQQQgghhMiX10jx2lBRFGU1UMnNR+Nu5HcpilIZmAP0UlXV4e1YabgIIYQQQgghboiqqm09faYoykVFUSrn9bZUxjkMzN1xocAyYLyqqpuK+p1lu59KCCGEEEII8U+zBOiV9+9ewOLrD1AURQ8sAmarqrqwOCeVhosQQgghhBD+pFHK9s/f9y7QTlGUI0C7vG0URWmqKMq0vGO6AXcDvRVF2Zn309jbSWWomBBCCCGEEOKmUVX1MnC/m/1bgf55//4G+OZGzis9LkIIIYQQQogyT3pchBBCCCGE8Keb8Hb6/0VltuGiiY3H9PxLaOs1Qs3MIHf5InJmfQUOz6ukaarXwDhkNNr4Wiih4agpl7Ft3Uj211+iXkn2Y/QFVFVlyrIE5v++iZTMLBrEVWFcj0eoWy262OdYvX0fwz+bQ/3qMXz/+nAfRltAExuPcfiLefmfiXX5InJmF5X/8RgHj0YTXxslNCwv/zeRM6OU83/rERbsOUGKJZcGUeG8ek9D6kaGeU234dQlftx/mp3nr3Auw8LQ5rUZ1qKOn6IGoqqieWIASvU6YMlC3fgrjpXzwdsqgVodmk49UarXhqo1UfQGbCMe9VfEhaiqypQV61jwxxZSMs00iI3h1e4dqFu1std0ny35ndU7DnDuShqqqhIXFUHfB1rRoVkDv8StRMeie3Y4mpr1wJyJPWE5tkWzvea9EncL2raPormlIUp4BdQrSdg3/oZ96bdgtfol7uupqsqUTftZsOMoKZYcGlQqz6ttm1A3qpzHNHaHgxmbD5Jw7BzHktMAqF+pPCPubkTDyhX8Erc2rgYhY8ehb3grjowMLIu/J2vql17rHheKQvlZ3xFQtz4po4aQu36tbwO+jjauBiFjxhGQF3/24u/JmnZj8Zeb6Yw/dbR/44+sEU+7sc8T36IZ0Q3qcXTdBia26VRkOmNoKN0+fpdbO3dCo9GwZ+kqFjz/IllXrvgh6sJUVWXKtqMs2HuyoN5vXb8Y9X4SPx44zc4LKc56v1ktht1xi5+iBk21OIzDXkRbtxFqVgbWFT+RM2eK9+tubDzGQaPQxNdCCQlDTb2CbdsmcmZOKt3r7g/LmL9yDSkZmTSoGce4/k9TN66a13SvfDqdn9b8WWj/ss/eJr6K9+uG+N9RNhsuwSEEfTAZx8njmMePQhNdBeOQF0DRkPP1Fx6TKUHBOM6fxfrLUhzJSWgqx2DsNYig2vXIHNwDHHY//hFOU5clMGnJb4zt1pG4ypHMXLWevu9PY8lbo4gMCykyfY7VyoT5y4gIDfZDtHmCQwh8Py//XxuFJroqxsGjQaOQ8/WXHpMpQSE4Lpwj95elqJed+W94dhDa2nXJGtKzdPJ/61Embz7MmLvqE1cumFk7jtFv0UYW97iXyCCjx3TrTl7iUHI6LapGsuLwWT9GDJiC0D73JuqF0zimvQ0RldE82geNouBYPtdzOr0B5c52qCePwImDKLVv9V/MbkxduY7Jy9cy5vEHiKsUwazVG+n30WwWvz7Ua9nPys6hc8vG1KhcEa1G4Zdt+3lh2kI0GoUHm9T3bdCBwehffh/H2ZNYP34NpWI0uqcHg0aD7fuvPSbTtmiDpmI09qXzUS+cQakWj+7xPmiqxmP99A3fxuzB1E37mbxhH2PubUxchVBmbTlIvwW/s7hvRyKDTW7TZNvsTNu0ny4N4xnQoh4KMG/7EXrOXc28nu2oX6m8T2NWQkIp98V0bInHSH1hGNoq1QgZORYUDVmTPy3WOUydn0BTMcqncXqihIQS/vl07InHSB2TF/+IsaApfvzGR0sv/uj6dWjQ8QESN21Bq9cXO92ABTOJuqUm3/Qfjupw0GXCmwz+aR4f3t3eh9F6NnXbMSZvOcKYVnWd9f7O4/RbvInFT93jvd4/lcShy+m0qBLBiiPn/Bgxzuvue5Oc193XRzvvewaOAkUhZ+Ykj8mUoGDndffXZXnX3WgMPQeirVWHrOeeLZ3r7o/LmbTwZ8b26kZcTCVmLvmFvq9/wJJP/kNkOe+Nx/iYyrw9vK/LvpiKEb4MV/zDlMmGi+GRrigGI1n/egHMWbANCArG2GsQOfNnOve5Yd+3C/u+XQXbu7aiJl0k6IPJaGrUwnHkoH/+gDw5VitTlycwoNO99GjbEoDGNWNpO2YCc1dvYOTjDxZ5jukr/iAqPJSqFctz5OxFX4cMgP7hrigGA+bXnflv3/YXSmAQhl6DyJk/6wbyfxuOpIsEvV9K+W+zM23bEQY0rUWPW+MAaFy5HO1mrGbe7kRG3FnXY9qxd9XnpdbObtzfj1/wS7xXKa3aQ4Aex/T/Qo4FDu3CYTCh6fAU/Pajc587lizsr/RwnqN1RyjFhkuO1cq0lesZ0L41PdrcAUDj+Kq0e/Uj5q3ZzIjOhebr5Xu5WweX7Vb1anL0/CWWbNrl84aL9v6HQW/A+snrkG0GtoEpEF2XXtiWzs/bV5ht6beQkVaw4+AusOYS0PcFrBWi4LJ/vrtX5djsTNt0gAEt6tGjSW0AGkdH0G7yEuZtP8KIuxu5TWfUaVk1+BHCjAU3rS2qR9FxyjLmbjvMO51a+DRu0+NPohgMpL34PGpWFmzeiBIURPDA5zDPme7c54USEkrwkBFkfD6RsNfe8mms7pgey4v/JWf81s0b0QQFETTgxuLP/GIioeP9H//un1ewa8lyAAYunE1wRNG9bHEtmlO/fVs+uLs9R9dtACD17Hle3ryGOvffy8HfEnwZciE5NjvTth9lQJOa9GiUV+9XKke7Wb8zb88JRnjpOR/bqi4v3VUPgN8T/Vvv6x96AkVvwPzvsc7r7va86+4zg8j5brbn6+7+3dj37y7Y3r0NR9IlgiZ8iSa+Fo6jfr7u5lqZ+uNyBjzWiR4dnfV841tq0nbQWOYu/42RPR7zmt5k1NP4lhr+CLX0KTLNvCTKZK7pmrfCumWDyxfV+vtKFKMJ3a1NbuhcjvRUABRdwE2NsTh2HDlJpiWHDs0LbhICDXraNK7Duj2Hi0x/7nIq01es5ZWnH/ZlmIXo7miFbctG1/xfs6pE+a+mO2/mSiX/z18hM9dG+1oFw/ICA3TcGx/FuhNu34OUT1OKY081dZugHtzh0kBRd6xD0RtQavpnuNTftePYaTKzc2jftKChEWjQc2+jW1i378gNny88KBCrzfdPDrWN7sCxZ4tLA8W+aQ2KwYimrpeG4LWNljyOE0cBUELDb3qcRdlxNonMXCvt6xQMzQjU67i3ZjTrjnt+kqzVaFwaLQB6rZaaEWFcMef4LN6rDHe2JnfTny43+Nm/rEAxmgi4vVmR6YMHDyd31w5ytxT5DjOf0Lf0Ev9tRccfNGg41t2lF7+qqjecpkGHdqRduJjfaAE4sWUbSccTqd+h3c0Mr1h2XEhxX+/HRbHuZJLXtKVZ7+uatcS29frr7i8oRiO6Rrff0Lnyr7sB/n82vePgUTLNFjq0KijvgUYDbZo2Zt32PX6PR/z/UyYbLppqcThOnXDZp166gGqxoKkWV/QJFAV0OjRVYzEOGIHtwF7sB/f6Jlgvjl9IQqvREBvl2s0ZH12RxPPeb5wB3pu/jP9j777Do6jWB45/Z3ezm91UIIGQACmAAgmKIIqiYAFpIqJiAwWlCBZABRvYL16x3qvyo3oFEaWpFAUCKB1RBKQjLQhSQgukbza75/fHhoRNdrNJyG7Cve/neXjITOZM3pycPTNnTpnOra8iMS7GVyG6pasfh+NIisu+ovyP834Cl/wfin1P1eR/Slomek0jNtx1mF1CjRBS0jL9Hk+Z1YmB1L9d96WdRllzoU69qompnFJOnHaW/dquT2wToiJIOVG2cdf5djvp2Tks/HUb63Yd4P721/oiVBdadH3UsSOuO8+cRFlz0OqWPj67OF3jRJTDjjp+xPvBlSzlTIaz7NcoVvZrhZFyNr1c58rLt7Mz9SwNI0IrM0S3DHHx5B9yrXscqcdROdkYYhNKT9voCszde5L57/d9GWLpMcTGY//LQ/xxpcevrwbxV0RUk8ak7in5IO7E7r1ENbnC7/EU1vthQS77E2oEV+t633ndPeSyT50quO7Wj/N+ggvX3XqxBA54puC6u9MnsZbm4NHjzrq/rutwx4R6dUk5etxr+gNHjnPtw09yVa9B9H75HX7b8aevQhWXqWo5VEwLCUFlZpTYrzLT0UK8Xzwt735GwHVtAcj/cyfZLz0NFXiSdKnSs3KwmIzoda7tw1CLmZw8G3n5+RgN7v8Ev+4+wNode1n87gh/hOqi1PwPLkP+//NTDAX5b/9zF9kvV03+n7fasATo0Rd7kVKYKYCcfDt5dgdGfTVsu1uCUTluhgXkZKJZgvB/Tpbf+Wz3ZT8syHvZB9h68AgPjXW+n8qg0zHqoa50aOF5aF+lsYSgst3c3GRlogWVY55ZWA0MPXrjWLfM4/AyXzqfm4fFaCiZ/4FGcmx28ux2jHp9mc418ZedpOfmcd9Vvh++oYWGojJLNqwc6enoQkuve0JGjiJ7zjfY/z6Mrm7ZFz+pTFpoKI4M9/F7u3aFjBhF9tyqjb8iLDXCyT5XsscxO+0cEQlxfo/ncq33tZDQS7vvGfMJhtbOIen2vbvIHjW0au57MrOwBJrQF8vj0OAgcqx55NnyMXroCWoa34CrrkigYb1o0tIz+GJ+MgPe/ICvxrzMVVeU3vC/LFXOSx7/53htuGialgS8ADQDFLAL+FApta3UhJfM3QdOK9MHMfeTsVhDQ9HFNMD0yECCxo4j8+l+YMur9CgvUEphv2jlD62gy9ldz/OF30DDfaHNt9sZM2MBg7vfVqYJ/D7hLp+1Mub/p++hhYSiq9cAY58BWN79jKxnHvN9/l8U24W81dz8AYryvzrzVP79HohX5Sr7BX8jd3+XizWOqcPslweRnpPL6u17GfPNIoIDA+l2XfPKC9wTt2W8bGUfAL0B49Ovo3JzsH3leTGLylKy7Lv+X/xY5/fKVvpXHTjKxF928cKtzgn+/uB2uJKmlTqMydSxC/rYeM4996QPIysjT3VnKR/eC/Gff74axF8Rbn5nrYzXi0v7seWp9y+U/ctMWa+7495DCwlDF1Mf48MDsIz5lKzhj/v3vqe0/C+s+z2f79HurkML27e6ijuHjmbStz/y2cv+WVFVVH+lNlw0TesBfAD8s+B/DWgFfKtp2gil1HwP6QYBgwAmTpzIA+UMSmVkoAWVvGHXgoPdPpEoznH0MBwF++4d5G/fQsjXPxLQoQu2xW7DrRQb/zxI37GTC7dbXxlP5+uuIis3D7vD4fLkMyM7B7MxgACD+yeec1ZtJCM7l7vbtiQ92znPwZZvx+FwkJ6dg9lo9Ji2MqiMDAh2k/9BwaisMuY/YN/jzP/gGT8QcHsXbEt8mP9Hz9Dvu6Ix1q1jatG5cTRZefnYHcrl6Vu61YbZoCegGj51AyA7E8xBJfebLe57YqrYxr2H6PfR1MLt1lfE0blVotuyn56d6yz7Xp72W0xGkgqGSN7YtCEZOVY+/G6Z7xsu2RngrmfFEoTyMDm2uIDBL6HFxJL39lDn39LHNh45Sb9vfi7cbl2/Np2bNCgo+8Xy32rDHFC2sr/9+Bmem7+e+1s05NHW/lkKXKWno3PTq6sFhzjrJXf0BkKGjSB72hTQdGjBIegK/oaa2YxmsaCy/dPrpTz0rHiLP3joCLK/LIr/Qu+eFujf+CsiO+0cwZElV30yh4e57YmpTBuPnqHfvKL5QK2ja5ZS7+dX63pfZaS7rXu0oLLe9xwBjhRdd6cvJOC2ztiSF/ggWqeNO/+k76vvFW63TrySzm1bk5WTi93ucOl1ycjKxmwyElBKT3txgSYj7Vo2Z+XvW70fLP5neCtBbwEdlVKHLtq3VdO0n4H5Bf9KUEpNAiZd2Dz/TfmeOjoOp5SYS6FF1kEzW3AcTnGfyAOVehyVcR5dXd/ODUiMrcec154u3A4KNJGadh67w8Hh1DPE140s/N7B46eIr1vb47lSTpziRNp5bh4+psT3rn/qTcYOfIC7brymcn+BiziOHEJfbC5RUf4fKte5CvM/2rfzdBJrhzP7gXaF20FGA6mZOdiV4vD5LOIvGuufkpbpsl3tpB5Fq13P9flseASayVxy7ks1kBgbzeyXBxVuBwWaSD2X7iz7J88SH1V0U5OSetplu6yaNajL9+u3YLPbvTZ6LoU6dgRd3Qa4LANQMxIt0Iw6fthrekPvp9C1bEve2JF+m9uSWKcmsx+9o3A7yBhAama2s+ynZbr0lKScSSe+pveek0Nn0xkydxVtYuswqkP5FuS4FPmHUjDEudY9ujpR6CwW8v866DaNZjajr1OXkOdeIuS5l1y+F/7OR+QfOcyZe/yzLG/+XykYYovFX7sg/kNe4n/2JUKedY0/rCD+s/dWzbLCZXFizz5uuvnGEvujmlzBH/N+8OnPTqwdzuxeNxVuBxkNpGblXpb1vvO6G+eyr/C6W2zuizfq5AlURjq6uj6+7jaMZc77rxZuB5kDST1zzln3n0glPqbo3SsHjx532S6X/9YXNcqqYhXireESUKzRAoBS6pCmaT5bJir/t3UYH+gLZgvkOJ80BdzaCZWbQ/7WTeU6l65+LLqwGjhO+PZdHEFmE0nxro2j6Ihwgs0mlmzcxpC7nMsC5ljzWPnHbnq1v87juXrffgMdWjZz2Tf5x1X8ffosb/btSUIpjZ7KkP/rOkwPPFos/++4tPw/7ts18YOMBpLquK7eFB1iJthoIHnfMQZf55wkmmPLZ2XKCXolxfo0nkvh2L0J3W09wWQuXFlMu+YmVJ4Vtd//ixx4ExRoKuwduSC6VhjBgSaSN+1kcLf2AOTk5bFy21563VT+G+EtBw4TVSPUp40WAPu2XzF0fQACzZDrzHt9m1tR1lwcu0t/6qfv/hD6O+7G9ulbqL3++zsFmQJIKvZyyOiwIIKNAST/eZjBNzpXosux5bPywFF6Xd2o1POdysxh4OyV1A8P5oO7biwxT8aXrL+sIajP4y69DIEduziH3W3e6DaNysnm7BN9XfbpakUQ/s6HZIz7mLyNv/o87gvy1q/BUix+04X4t3iOP21wyfjDxnxI5riPyfvdf/FXxI7Fy+j22os0bNuGA+ucvR8NWl1DZMN4di5e5tOfXWq9v/84g1s3BiDHZmfloVR6JZZvgQ1/yt+4HlOvR1yvu+3vQOXmkr9tc7nOpasXiy4sHMcJH193zWaSGrk21KMjIwi2mFmy/neG9HKuiJpjtbJy41Z63dG+XOfPteaxZst2EhOq7/Va+J+3hotN07QGSimXR42apsUC+b4KyrpgDsZ7HsLy1kdYv/nC+SKmfoOxzvnKZanA4K8WYN+6iZz33wQgcPCzKLsd++7tqMwM9LEJmB7sh/3oYWw/L/FVuB6ZAgIY2PUWxi/8mdAgCwl1I5mavAaHUvTpUPSEat66TYz+z7ckjx1JTEQNYutElFiJ7Pu1m0jLzOK6Jr6fIJu3cA7Gex7E8uaHWGdOdb5Isu9g8uYWy//p88nfupncD5z5bxr8LFyU/7oG8Zge7Ifj6GFsK6og/w16BrRqzISNewk1BRBf0/kCSoei8L0uAPN3H2H08j9Y0vd2YkItABxNz2ZHqnMpbZvdwYGzmSTvO4Y5QE+7ON++HE6tWwLt7kTX/yXU8u+gVh10XR5ErZjvskSyfvQE1IEdOL75rHCf1rQlGAPRYpwTGbWrneVMHd4HaaUvBVqZTAEBDOh8ExN+XE2oJZD4qEimLV+Pw6Hofdv1hcfN/+UPRn85nyX/GEZMrXCOnjnH6Gnz6HZdc+pF1CDbmsfyLbtZtHEHrz18p8/jtv+0EMMd9xAw7E3sP8xEq10XQ8++2JfMdZlkb/xgOo49W8mf8gEAuhtuI+D+geSvXoJKO43WsGghAXXymNvlkn3JZNAzoE1TJqzfSWigkfiaoUzb+CcOpejdqnHhcfN3pDB60a8seaI7MWFB5NryGTRnJem5eYzu2Iq9J88VHhtg0NGsjm9fQJnz7SwsD/Qh7L1PyP7yc/Qx9Qga+BRZM6a5LDFc67sl2DZvJP0fr4LdXqJRc2Fye/7+veTv9PF0zIvj/24W5gf6EDb2E7Iuij/7a9f4a367BNuWjWR4i/+Af+MPMJtp3tXZexceE01gaAgt7+0BwPZFS7Hl5PDWvj/Yt2ot0wc4RxikbPiNnUuW0+/LiXw7YnThCyj3rVnv93e4QEHZb9mICb8X1PsFL6B0KFX4XheA+Xv+ZvRPW1nyyK2u9f7JC/W+4kBaJsn7j2EOMNAu1rcPDPN+mIvx7gexvP4+1lnTCl7gPIi8b4tdd6fOI3/bJnI/etv5+w4a7rzu7tlRdN29/1EcR49gW5ns05jdMRkDGHhPV8bPXui876lXl6kLljrve7oVvb9r3op1jP7sC5LHv0tM7QgysrIZMubfdG9/Aw3q1iYtPZNpC5eSeuYcH48Y4vffQ1Rf3hourwPLNU17B+drIBXQGngJeNFnUWVmkPX8E5iHvkTQO/9GZWZgnfMV1mkTXA7T9AbQFT2Btf+5C+M9D2G88140oxHHyRPYVi/HOuM/kJvrs3BLM7DbLTiUYvKPKziXmU1SXD0+H9GfiIsm3RdNcKsmM68zM8geMZjAoS9iGfMvVGYGeXNnlMh/9Aa46Gms/c9dGHs+iLHbPVCY/z+R93UV5v+1jXCgmPz7Ps7l5pFYJ5wpd99AhKXo7cmOYhM8AX77+zSjlv9RuJ28/xjJ+48RHWJm+WM+fjdBThb2ca+hu28QuoGjICcLtXIBjsUzXY/T6Up0Net6DUarVdSw0j/u/JjaZ/wb9dvP+NPAzjc7y/6StZzLyiYxNpopwx8lIrRouIbjQtkvyP9QSyCRYSFMWLSa0+czCbEE0qhuJOOf7k375n5YWjU7k7x/jsDQdygBz42B7EzsS+aS/900l8M0nR7torKvb+5cqtnQrjOGYm8Lt00ai32N/28gBrZphkPB5F92Oct+VE2mPHArEUHmwmOKyr4z/89k5/JnwY3bkLmrXc4XHRrE8iF3+TRmlZFO2pOPEzpyFOEfjsORmUH2N9PImjTO5ThNr4dqOFdBZaRz7qnHCbko/pxvppE12U38fuzJKqvQ2pEMmjvdZd+F7VFxSZz56zB6gx5dsZ7PKQ8+Rq+P/8mj/xmHptOx/YdkZg0d6be4ixvYqqGz3t+031n2a4czpUcbIiymwmPc1vtHzzDqp6Ke1eT9x0nef9xZ7/f1/NLcSpGZQfYLgwl8+kUsb3+Myswk79uvsU6f6HqcXu/8V8C+dxfGux/A2LUnGE3O6+7an8n75ouqu+7e0xWHw8Hk7xZxLiOTpIZxfP7G80SEhxUeU3xivzEggBqhIUyYs5Az5zMwGQNocWVDvvzHiyV6dcT/Ns3bC6c0TbsaeB5IxDk5fyfwgVKqrLOl1PlbW1xSkFUlbIXzxtWx/vsqjqRidDf2BCD9Nt/Nh/Gl0J+3YB9XdRe/S6V/6n3y9/xWwAAAIABJREFUh/Wo6jAqzPDv+dhXzvR+YDWkv+VBAHIfua2KI6mYwOnORqb9P29UbSAVpH/8DQBSWzcr/cBqqs7GXZy87vKMHaD2b7sYrPlnFThfmKDSsX/6fFWHUSH6Zz4EIL2j/+aGVabQZc7h4I5d66o4korRNWsLl8nicfZ5n1WTp9Xu6e9+ulrmo9flHQoaKI/6IRYhhBBCCCGEcMvbcsgLKWX8klLKt+MGhBBCCCGEEALvPS4f+CUKIYQQQggh/lfIcsgV4q3h8phSqp8/AhFCCCGEEEIIT7w1967ySxRCCCGEEEIIUQpvPS4WTdOuwcMKDUqp8r0VSQghhBBCiP91umq5aFe1563hEgN8iPuGiwIuz7VGhRBCCCGEEJcVbw2X/UopaZwIIYQQQgghqpTX97gIIYQQQgghKpGsKlYh3nLtRXc7NU2rr2na5ftKcyGEEEIIIcRlpdSGi1Jq6YWvNU2L0DRtiKZpq4GVQB0fxyaEEEIIIYQQgJehYpqmhQA9gYeBK4DvgQSlVD0/xCaEEEIIIcR/H01WFasIb3NcTgK/AaOBtUoppWlaT9+HJYQQQgghhBBFvM1xeQUIBMYDL2ua1tD3IQkhhBBCCCGEK29zXD5WSl0P3IXzXS7zgGhN017UNO0KfwQohBBCCCGEEGVai00pdVApNUYp1RxoDYQDi30amRBCCCGEEP+NdLrq/a+aKvd7XJRS2zVNexXY6YN4hBBCCCGEEKIETSnl+ZuaFgo8BcQAC4BlwNPA88BWpVSPMvwMzz9ACCGEEEKIynNZLNdlXzylWt8f67sMqJb56K3HZTqQBvwCDABGAkbgbqXUH2X9IY7NS70fVA3pWt4BgG1Q5yqOpGICJi0BwP7zjCqOpGL0t/XG/ka/qg6jwvRvTMW+7ruqDqPC9G3vIX/oXVUdRoUYPlkAgH3uv6o4korR3zccgOz7bqriSCrGMnctAPa1c6s4korR33QfjvXfV3UYFaa7sSf2T5+v6jAqTP/MhwzWQqs6jAqZoNIBsCd/UcWRVIy+02MAZHZvU8WRVEzwwg1VHULZyXLIFeKt4ZJQMK8FTdOmAKeBBkqpDJ9HJoQQQgghhBAFvM2+sV34QillB1Kk0SKEEEIIIYTwN289LldrmpZe8LUGmAu2NUAppS7PvlwhhBBCCCGqilZ9V+6qzrw1XAKVUjYvxwghhBBCCCGET3lr7v3qlyiEEEIIIYQQohTeelxkyQMhhBBCCCEqk6wqViHeGi6RmqY95+mbSqmPKjkeIYQQQgghhCjBW8NFDwQjPS9CCCGEEEKIKuSt4XJcKfWWXyIRQgghhBDif4FOVhWrCG+5Jj0tQgghhBBCiCrnrcelm6Zpw4FGwHbgc6VUvu/DEkIIIYQQQogi3houHwM2YA3QBWgGDPN1UEIIIYQQQvzXklXFKsRbw6WZUqo5gKZpnwO/+T4kIYQQQgghhHDlbY6L7cIXMkRMCCGEEEIIUVW89bhcrWlaesHXGmAu2NYApZQK9Wl0QgghhBBCCIGXhotSSu+vQIQQQgghhPifoMlyyBXhrcelyimlmDR/KTOXrSUtI4ukhg0Y1fc+msbVK/M5lm/cyjMfTSExvj5z33nBh9FepG4D9A8OQWvYFLKzcKxdgmPhDFAOj0m02CvQ3XInWuMkCK8JZ0/j+G0FjiWzId/mMZ0vKaWYtGQts9ZsIi0zm6TYaF65vzNN60eVmu7ThStZ/scejp09h1IQX6cWj3e8kS7XJvon8MhodF36QP2GkJuN2rwatXIeKFV6OpMZrfPDaE1agqah9m5FLf4KcrL8E3cxSikm/biSWSt+JS0zi6T4erzycHeaNogu8zmWb97J0M++IjE2hjmvP+3DaAtE1Ud37yC0+CaQk4n6ZRmOxTNLLfvoDeju7IMWdyXUb4RmNJE/9C7fx1oKpRSTVm1m1m87ScvKJalebV7pdhNNoyM8prE7HHyxdisr9xziwMk0ABJjIhnW8Xqa16vtl7i1enEY+w9Hd0USKisT+08Lsc35Ahye81/XsAmGTj3RNb0arWYE6vRJ8tcuI3/eDLDl+SXu4pRSTFq0ilkrfnOW/bh6vPJwt3KW/V0MHTeDxNho5rz2lA+jLenCZ3fmzxsKP7ujet9V7s/uM59OJzEuhrmvP+PDaEtSSjFp035m7fiLtJw8kuqE88rNiTSNDCs13frDp/hu9xH+OJHGsYwcnmzdmKevv9IvMUc2TKDjyKEktGlNdFIz9q9Zz0e3dvOaLjA0lPv/9S5X390NnU7H9h+SmTX0BbLOnvVD1CUppZi07Bdmrd1CWlYOSQ2ieOXejjStV6fUdJ8uWsPyrXs5dvY8CoivXZPHb7+eLi2b+iVurX4cpieeR9+kOSozg/xlC8j75vPS654G8RgfH4ourhFaaBjq3FnsW34j76uJqLQzfolbXF6qfXNv8vxljP8umQF3deD/Rg7CEmji8TGfcepcuvfEgDXPxtivviciLMTHkV7EEozh2X8CYB/3Jo4fZqDreC+6ux4pNZnWuh1E1sW+ZDb2T17DsXIhuo73oB/woj+idmty8jomLF5D/ztuZNyQB7GYjPT/93ROnc8sNV1WrpW7b7iaD/vfy78H9aJZg7o8//m3JG/e5fugAy3oHh0JKBzffIJatQDths5ot/b0mlTX60m0uCY4FnyBY94UtJh4dA8O9XnInkxetIoJC3+mf9d2jBva15n/H3zOqfMZZUpvtdl4b+aP1AoN9nGkBcxB6J96C1A4Jo/BsWQW2q13o+v6UOnpjCa0G+5A5VlRKXv8Eqo3k1dvYcKKTfS/+RrGPdIVizGA/l8s5FRGtsc0uTY7U1Ztpnm92rzb63bG9rodg05Hn0nfs/PoKd8HHRSC6bV/gQLr2JfIn/sFhu4PEnB//1KT6dvejhYVg23+DKzvjCQ/+TsC7nwA47DXfB+zB5MXrWbCwhX073Iz44Y+giXQSP8Pvyhf2Z+1yH9lv5jJP65k/IKfGNC1Pf83rC8Wk4nH359SrvjHzvyRiKqKf9MBJmzcR/+WDRl3Z2ssAXr6z9/AqazcUtOtOXyKP8+k06ZeBGaDfwdtRCc2IanrHaTu3U/q3v1lTjdw1lSuuOUmvhrwDNP6DSG2dUsGz/vah5GWbvKyDUxIXk//Dm0YN+g+Z70/bian0stw3b0+iQ8f68G/H+9Js/p1eH7qfJK3+KFODQrB/PanoCD3Hy+QN/M/BNz9MMaHB5aezhKMSj1O3hefkvvacPJmTEZ/dWsCX/8IdDLoR5RUrXtcrHk2Ji9YxsAeHendqT0ALRrH02HoG8xIXs3wB+70eo7Pf/iJOjXCqF8ngn1Hjvs6ZAB07bpBgBH7+LedT/t3bwGzBV33PjiS50Cu+xsfx5I5kHm+cFvt3Yay5WF4ZBj2mrXh7Em/xH+B1ZbPlOR1DOzUlt63XAdAi4R6dBz9CV+v/I1hPW7zmPalXp1ctts2a8j+YydZsGEbnVo282nc2rW3gsGIY9anYM1FHdwJpkC0W+5GrVsEVg8X3noN0Ro1x/7FO/DXXgAcGWnoB74OCc3goB8aXRex2mxMWbSSgd1uofftNwLQolEDOo58j69/+oVh99zh9Rz/Wbya2jXCaFC7Jvv+TvV1yGg3dYEAE47P/wm5OfAnOAIt6Lo8BD9959znTk4W9pcedp7j5m5w5dU+j7U0Vls+U1ZtYWD7lvS+oTkALRrUoeP7X/H1hu0M63i923SBAXqSR/QhzGwq3NemYT26fvwNMzZs5517PX9mKoPhjh5oRhO5778COdk4tv0O5iAC7n8c2/wZkOO+7rHNmwHp5wq3HTu3oPLyMA1+AVtEHdRp35edi1ltNqYsXsXAru3pffsNALRo2ICOL7zP1z9tYNg9Hb2e4z9L1lC7RigNImuy76j/45984bPb4cJnN5YOI8YyY/l6ht/bycsZ4PPFq6kTHkr92lUQf76dKZv3M7BVI3pfFQ9Ai6gadJz2M19vP8SwNk08ph3Ztikv3uSs439OOeGXeC/YtnAxWxcsAmDQnC8JjqjlNU18m+tI7NyBD9p1Zv+a9QCcO3qcl35bQZPbb2HPTyt9GXIJVls+U5ZvYGCHNvRu1wqAFnHRdHxjPF+v3sywO9t5TPvSPR1ctts2jWf/8dMs2LiDTtd4/ptVhoAuPdFMJnLfebGwntEsQRgfHkDet9M91j2OPdux7tletGMHqDOnML/9Cbr4RjgO/OnTuKuULIdcIdW6x2XL3oNk5uTSpc01hfssgSZubZnEmq3ebyKPnT7L5wuX83Lf+3wZZgla0rWoXZtcGiiOjavQjIFoVzT3nPCiRkuhwwec5wwJr+wwvdpy4AiZuVY6tyoa3mUxGbml+RWs2Xmg3OcLD7Jgs9srM0S3tMZXoQ7scGmgqB2/ogWYINZz5a01vgqVeb6w0QLA0RRU2km0Rlf5MmS3tuw/TGaOlc6ti8qMxWTklhZNWLPde2V+7Mw5Pl+8mlce9t7Aryy6pq1Quze7NFDU5tVoRhNaoyS/xXGpthw+QaY1j87NGxbusxgDuKVJHGv2HvaYTq/TuTRaAIwGPY1q1+BspodGWyXSX9MG+9ZfXW4S7Ot+QjMFom92jeeEFzVaLlApzs+BFlaj0uP0xmPZv7oJa3bsLSWlk7Psr+GVh7wPE/KFLfv+IjPHSpfriuoNi8nIrS2asGZ7WeNfxcsPd/dlmB5tOZFGZl4+nRsXDWuzBBi4Jb4Oa/4qvedQV4U3Y8rbUGA3krp05PyJ1MJGC8ChjZs4dTCFxC7eG8iVbUvK387r7kXDuywmI7ckNWLNropcd83Y8n1/3TW0uoH8za51T/6aZc66J6lluc6lMgruhQzV+tm6qCLVuuFy8Fgqep2O2LquY8MTYuqQcsz7E6j3vvqezm2uITG+vq9CdEuLqo868bfrzrOnUNZctKjyxaI1bIpy2FGpf3s/uJKlpJ5Gr9OIrV3TZX9CVAQpqafLdI58u4P07FwW/raddbsPcP/NrXwRqquIunC6WO/a+bOoPCtaRN3ypQM4dbz0dD6Scvyks/zXcZ1TkVC3NinHvQ87em/Wj3RufRXNYmN8FWJJdWLg5FHXfWmnUdZcqF32eWlVLeXUOWfZr+U6nj+hdjgpp0re5JcmL9/OzmOnaFjb9w0AXUws6qhrw0qdTkXl5qDFNCjfua5sjrLbcRzz3FDzlZTjpwrKvusT84Tospb9xXRu3dy/Zf8iB0+ccv/Zja5NynHvPefvzXR+dhPjqib+lLRM9JpGbFiQy/6EGsGkpJU+XOlyE9WkMal7SjYmT+zeS1STK/weT0rqWWfdE+laXyRERZBysmxzbgqvuxt3sm5PCvffVMpDi0qi1YtF/f2Xyz51yln36OrFluEEGhgMaDENMPZ9EvvenTj2+neUg7g8lLs5q2laBHBGVeTRRjmlZ+VgCTSh17m2r0KDLORY88jLz8fooUX+6869rN22h8UfverrMEsKCoZsN5V7dgZYyjFeObQGuq4PoTb87HF4mS+dz87FYjKWyP8wSyA5eTby8u0YSxnDvPXg3zz0/n8AMOh0jHqwCx1a+La7GoBAi/v8ys0Cs8VjMs1DOpWbhVYjsjIjLJPzWR7yP8hckP+llP/dB1i3Yx+L/vm8P0ItYglGuVvIICcTzRKMzyuNSnI+x4rFGFAy7wNN5NjyvZb9i01cuYn0HCv3XeuHCbJBIaisknMoVFYGWnA55vmF1yTg3kexr072OMTDl85n53ipe0op+3sOsm7nPha986w/QnUrPct9/KGWsn121+7Yy+J3R/gjVLfOW21YAvToda69J2GmAHLy7eTZHRj11fq5Z5lZaoSTfa7kaIfstHNEJMT5PR6P111zGa+7KUd56OPpQMF1t1dHOlzl+waYFhzqvu7JLFvdE/j6RxhaOYeF2vftJvfN57wvpnO5k1XFKqTUhoumaW2Ad4GzwNvAdCAC0Gma9qhSaomHdIOAQQATJ05kwLVxXgNRSmG/aOUJraC72V2v84U2k4b7Lul8u50x0+Yy+O5ORIZX0atm3H7gNCjrrZvegH7QK2DNwT57YmVG5pYz/4tiu5Dv7vJYFTvGk8YxtZn90gDSs3NZvWMfY2YuJjjQRLfWfhgy5Cn/vWV/RdNdosou/+98vZDB3W8l0p+LUlzgMQ+r50XIc9l3c2yxY7xZtecvJq7czAtdbiA+0k9DrtxmcznKsMGA6bm3ULk55E39tBIDc698Zb/gmFLL/g8MvvMWv5X9csV/4ZjSrl0zFjC4+23+jf+iz+aF2DQ3v4DiQt3zX8ZN3aRpvq+zylf3KJdjPGkcHcnsEX1Jz7Gyeud+xsxZ5rzutvLt3FJnkG7yq4yFxTrxI/JCQtFF18f4QD8C3/iYnBcGVdmqhqL68tbj8hnwChAG/Ax0UUpt0DStCfAN4LbhopSaBEy6sOnYvNRrIBt376fv258Ubrdu2ojOba4hK8eK3eFwefqQkZ2D2WQkwMNThzk/rycjK4e7211HepbzaaEt347DoUjPysZsMnlMWymyMt33rJiDILtsy+rqHx+BFh1L/nvPue+9qWQb9/1Fv4+/LNxu3TiWzq2akWUtmf/p2bmYjQEE6EvPQ4vJSFKsc4z0jU0TyMix8uH3y33fcMnNdva6FBdoLrXnSuVmo1lK3ix46ompTBv/TKHfe5MLt1tfGU/n1s3Jys3znP8eyvDc1RvJyM6lx40tSc92zquw5duxKwfp2TmYjZ4/O5csO9NZzoszW9z3xFQDG1OO0e/zBYXbreOj6ZzUkKw8W8m8z7ViDjB4LfsA2/8+yXOzlnJ/62Y82tZPiw1kZTh7fIvRLEFun4a6Y3xmNLr68eSOGuI8n49t/DOFfu9/Xrjd+sp4Ol+b5L7s53gr+7+XLPt2O3aH8lnZ3/jnQfqOLfbZve4qt/FnZOeUGv+cVc7P7t1tXT+7DofvPrsbj56h37wNRfFH16Rz42iy8vKxO5RLr0u6NR+zQU/Af0lvCzh7VoIjSy5xbg4Pc9sTU5k27j9Mv0+/Kdxu3ag+na9pSpbVXdm3lv2628A5tPnGK+Oc190FK33ecFGZ6RDk5vppCUZleq9H1PEjqOPg2LsT+84/sEz5DkP7O8hf/oMvwhWXMW8NF4NSaimApmlvKaU2ACil9rh7GnMpEuPrM+cfIwu3g8wmUs+ew+5wcPjEKeKji9YvP3gs1WW7uJRjJzlx9hw3DxlV4nvXD3iRsU8+yl03t67U+C+mThwpOZelRgRaoBl14ojX9Lr7n0C7+gbs/3oFis+V8ZHEBnWZ/dKAwu0gk5HUcxnYHYrDJ88SH1VUsaeknia+jud3WXjSrEEU3//yBza7vUw3fhV2+rhzvsrFQmuiGQNxuJvDcnG6lm661CPqovZsrtwYi0mMjWH2q0XvmQgKNJF6Lt1Z/lPPEF+3aKhayvFTLtvFpRw/xYm087R79p0S32vz9Fu8O/B+7rrBR2OeU4+i1ann+nA/PALNZIaT/p+nVRaJMZHMHnJv4XaQKYDU9Cxn2T9z3qWnJOXUOeIjvS+Ucej0OYZ8+SNtEuoxqvtNPonbHcfRv9DFuI4n12rVRjNbSsx9cSeg31D0196M9e1nUX6a2+Is+0MKt4MCTaSmFZT9k2eIjypH2T9x2ln2n/tnie+1eeYfvDugF3fd0KKS46/HnNeK3o/kjP+828/uweOniK/r+X0+KSecn92bh48p8b3rn3qTsQMf4K4bK/ezm1g7nNm9ispokNFAalYudqU4fD6L+BpFDeGUtEyX7f8GJ/bs46abbyyxP6rJFfwxz7c3zYn1o5g9om/hdpDJSOr5guvuqTTiL5rjlZJ6hvhi803Loln9KL7/dbvPr7vq779KzGXRIpx1j6PY3Bev5zp1AjLT0UVVzRwvv9H91/Vd+oW3hsvFbw0qviROpfahBpkDSWroOnk0OqImweZAlmzYwpB7OjuDsOaxcvMOet3W1uO5endqR4fWrqtATZ6/jL9PneHNAQ+SUEqjpzKoHb+j63QfmMxgdWab7tr2qLxc1N7tpabVdX4A3W13YZ/0Dmr/Tp/GebGgQFNh78gF0bXCCQ40kbx5F4O7OpdgzMmzsXLbXnrdVL5VQsC5SllUjVDfNloAtW8bWtsuKGMg5DlXFtOSrkPZrPCX5/Xs1b5t6Nr3gAaN4fA+587oOLSatXHs3+bTmIPMJpLiXSevR0eEE2w2kfz7dgZ3dy6jm2PNY+XW3fRqf53Hcz18+w3c3tL1RZ9TFq3k71NpvNG3Jwml3PhdKsfuTehu6+lS9rWWNznfz7J/h89+7qUIMhlJKvZyyOjwEIJNRpJ3HGTwrc4FJXLybKzcc4herUt/cnkqPYuBU3+gfs0wPnigQ4mx6r5k37KBgLseLuhddOa/vu3tKGsu9l1bSk1r6NkHQ5d7yfvoNRx7fFveLxZkNpFU7IXC0bUKyv7GHQzufitwoezvoVc7zw+dHr6tDbdf4zqXaMri1c6y/2gPEkppNFxS/B4+u0s2bmPIXbcXxf9H6Z/d3rffQIdiy8VP/nEVf58+y5t9e/omfqOBpDqujfHoEDPBRgPJ+48zuHVjZ/w2OysPpdIrsXyLPFR3OxYvo9trL9KwbRsOrHP2PDVodQ2RDePZuXiZT392UKCpsHfkguiaYc7r7h97GNzJeZ+Tk2dj5Y799Lqx/I3uLQf/Jio8xOfX3fxNv2C8pzdWs6VwXpzh5g7OumdH+R78aTEN0ELDcaQe80Wo4jLnreFytaZp6ThHKZoLvqZgO9CnkQEmYwAD7+rI+O+XEBpsISG6DlN//BmHQ9GnU9Fa5vNW/8roiV+T/K/XiYmsSWxUJLFRrjdn36/aQFpGJtc1a+zrsHGs/hHd7T3QD3nV+d6WiCjnO1yWfe8y5Mjwj/+g9m7H/uXHAGjX3YL+nsdwrFsKaWecbx4voE4dd79csg+ZAgwM6NSWCYtWE2oxEx9Vi2k/bcChFL1vLbr4zt+wldHTF7DkrWeIqRXO0TPnGP3lArq1TqJeRA2yrXks37qHRb/v5LWHuvo8bvX7CrTrO6J74Gkc6xah1Yh0vsPll2SXJZJ1Q8eiDv2JWuBcQIC/D6D2b0fXcyCOpbNAKXQdeqH++tPv73ABMAUEMKDrLUxY+LMz/+tGMm3pWmf+F7zbAmD+us2M/uJblrw7gpiIGsTWiSixmtG8dZtIy8jmuiYJPo1ZrV0M7e5E1/9l1PJvnWW/y0OoFfNclkjWvzoRtX8Hjm+K5lBoTVs637dTz/neCK2F8ymo+msfpPnh5Y0XMQUYGND+Gias2ESo2UR8ZDjT1m7FoSh8rwvA/C1/Mvq7FSx5rjcxNULIteUzaNqPpOdYGX3nzew9UfTm5wCDnmbRvl3kIX/pfAK63odp5DvY5s1AqxNNQK/HyF84y2WSfeCnM3Hs+oO88e8CoL+pI8beg8n/+UfU2dPoGhc1fB2pR90ul+xLpoAABnRpz4QfVhAaZCY+ykPZX7+F0V98x5J/PldQ9muVWIls3rrNpGVk+bzsF49/YNdbGL/wZ0KDLCTUjWRq8hocStGnQ9HT/XnrNjH6P9+SPHakx8/u92s3kZaZxXVNGhb/Mb6L36BnQMtGTPh9L6GmAOJrBDPtj4PO/C94rwvA/D1/M/qnrSx55FZiQp3Dc4+mZ7PjpLO82OyKA2mZJO8/hjnAQLvYym94XSzAbKZ5V+f7rcJjogkMDaHlvT0A2L5oKbacHN7a9wf7Vq1l+gBnL1nKht/YuWQ5/b6cyLcjRqMcDnqOfYt9a9b7/R0uUFD3dGjDhOR1hJoDia9Ti2krNjrzvl3Rqpzzf9vO6K8XseS1wcTUDOPo2fOMnvEj3VolUi8ijGyrjeXb9rJo825eu9/7e4MulW3x9wR0v5/Al9/F9u10tKhojA8NwDbvG5e6xzJxDvYdW7B+6hwRYHz8GbDbsf+5E7Iy0OrHY7ynD45jR8hf7duGo7g8ldpwUUpV+WtLB/boiEMpJs9fxrmMLJIS6vP5K08RcdGk+6LJkdVk8m92JvkfvYT+oSfRP/UG5GThWP49joVfuR6n08NFT2N1zZyVkq7tHejaur5cMP+LD1G/+P9DPLBTW2f+J6/lXFYOiQ3qMmVYH5e3OTuKTTAMtQQSGR7ChMVrOJ2eSYglkEZRkYx/6iHaJ/m+4UhuNo4v30PXtQ+6h4Y7XwL6y1LUyu9djyuW/wCOuePROj2Erkd/0DTU3j9Qi2f4PmYPBnZtj8OhmLxoJecys0mMq8eU5/sTcdHEXUexycFVKicL+7hX0d33BLpBoyEnC7ViAY7F37gep9OVyHvd/UPQahX1huoffwkA+1f/Qv32s89DL25gu2uceb9qM+eyc0mMiWTKY3cSEVw0f6qo7DvL/5nMbP4saKwMmb7I5XzR4SEsH9nHt0FnZZD75nCM/Z/F9NJYyM4g/8fZ2Gb/x/U4vWvZ11/t7MUw3NYNw22u7z6xfjYG+8rFvo3bjYFd2+FQDiYvWlVQ9mOY8txjRIRdVPc4qlHZL2Zgt1ucdeePKziXmU1SXD0+H+H62a12166LDGzVEAeKyZv2cy43j8Ta4Uzp0YYIS9F7ihzFJvYD/Hb0DKN+2lq4nbz/OMn7jxMdYmZ539t9GnNo7UgGzZ3usu/C9qi4JM78dRi9QY+uWO/DlAcfo9fH/+TR/4xD0+nY/kMys4aOpKoM7NjGWXaWbeBcdg6J9aOY8uQDRIQWzR8srHsK8j/UbCIyLIQJS9dxOj2LEHMgjaJqMf6JXrRP9EOjNyuDnNFPY3piBIGvvo/KysQ2fyZ530xxPU5vcNY/F36PfbsJuPN+Ajr1AKMJdeoE+etXkDd3mueXRf+3kFXFKkTzw6rGZZqcXx3pWjobD7ZBnas4kooJmORcO8H+c9XdeF8K/W29sb/siKOPAAAgAElEQVTRr6rDqDD9G1Oxr/uuqsOoMH3be8gfeldVh1Ehhk+ck+3tc/9VxZFUjP6+4QBk3+e/+TGVyTJ3LQD2tXOrOJKK0d90H47133s/sJrS3dgT+6d+Xgq9Eumf+ZDBWhWtCHqJJijnwBR78hdVHEnF6Ds9BkBm9zZVHEnFBC/cAJfJwnf2NXOq3xOLi+hv7lUt81Gae0IIIYQQQohqTxouQgghhBBCiGrP2+R8IYQQQgghRGWq5NeK/K+QHhchhBBCCCFEtScNFyGEEEIIIUS1J0PFhBBCCCGE8CdZDrlCJNeEEEIIIYQQ1Z40XIQQQgghhBDVngwVE0IIIYQQwo80WVWsQqTHRQghhBBCCFHtScNFCCGEEEIIUe3JUDEhhBBCCCH8SVYVqxDJNSGEEEIIIUS1Jw0XIYQQQgghRLUnQ8WEEEIIIYTwJxkqViGSa0IIIYQQQohqT1NK+fpn+PwHCCGEEEIIAVwWL0hxbFhYre+PdW26V8t8lKFiQgghhBBC+JOuWrYLqj2/NFyOXN3EHz+m0tXfugcAx6bkKo6kYnStOgGwsW6DKo6kYlofP4x9zMCqDqPC9KMm80f9uKoOo8JaHDmESj1Y1WFUiFYnAYDfLtOyf93xwwA4tiyv4kgqRndNBwDSO7Ss4kgqJnT5Zo5ec3letwBituwhvWOrqg6jwkKXbcKe/EVVh1Eh+k6PATBYC63iSCpmgkp3fpFxumoDqaiQiKqOQPiYzHERQgghhBBCVHvScBFCCCGEEEJUe9JwEUIIIYQQwp80XfX+d6m/nqbV1DRtmaZp+wr+r1HKsaGaph3VNO0zb+eVhosQQgghhBCiMr0E/KSUagz8VLDtydvAqrKcVBouQgghhBBCiMrUA5hW8PU04G53B2ma1gqoAywty0ml4SKEEEIIIYQ/aVq1/qdp2iBN036/6N+gcv6GdZRSxwEK/q9dMgs0HfAhMLKsJ5X3uAghhBBCCCEKKaUmAZNKO0bTtOVAlJtvjSrjj3kSWKSUOqJpZXuvjTRchBBCCCGEEOWilOrg6XuapqVqmlZXKXVc07S6wEk3h90A3Kxp2pNAMGDUNC1TKeVxPow0XIQQQgghhPCnSli5q5pbAPQF3i34f37xA5RSvS98rWlaP+Da0hotIHNchBBCCCGEEJXrXaCjpmn7gI4F22iadq2maVMqelLpcRFCCCGEEEJUGqXUGeB2N/t/Bwa42T8VmOrtvNJwEUIIIYQQwp/KOBlduJKhYkIIIYQQQohqTxouQgghhBBCiGpPhooJIYQQQgjhT//9q4r5RLVtuBgSGlLjpdEYr2qBysgg8/s5pE8YBw5H2U6gadT5ei7GZomcemYwuatX+jReT5RSTJq/jJnL15KWkUVSwwaMevRemsbVK/M5lm/cxjMfTyExvj5zx5T55aKXJPCKxsT+4y2Crm2J/Xw6p77+hmMf/qtM+V+ja2fqPvMU5iuvxJGTQ9bWrezv/wSOnBw/RF6SdmNXtFbtwRwMxw/hWDoTUo94T3jF1eja3w0160DaKdSahajdv/s8XgBT40bUe+tNglq1xJ6ezplvZnLi43+Xmv/GevVo9svaEvvTFizkr6ee8WW4bimlmPjVLGbO+5G08+k0b3IFo4YNpmnjhl7Tpp1P5+NJU/l53QYyMrOIjqrNE30e4O7OHpeMrzQXyn7wRWX/aDnLvqWg7GdWYdlXSjFpXjIzl60pqnv69aJpXP0yn2P5xq088+EkEhMaMPedF30YbRFdg3gCn34RfbPmqMxMbIu/xzp9Uqn5r4tNIHDwc+jiG6OFhqHOnSH/9w1Yp45HnT3tl7gvMCQ0JOyFgmtXZgZZ388hY2L5rl2RXzmvXWeGDiZ3zUqfxlucM/9fQN/0KlRWBrbF88qW/088iy6hMVpIGOrcWfI3VU3+Q0HZX/YLs9ZuIS0rh6QGUbxyb0ea1qtTarpPF61h+da9HDt7HgXE167J47dfT5eWTf0Sd2TDBDqOHEpCm9ZEJzVj/5r1fHRrN6/pAkNDuf9f73L13d3Q6XRs/yGZWUNfIOvsWT9EXZJSiolffMk3384j7dw5mjdryugRw2l65RWlprvy2rZu9wcEBLDjl5U+iFRcjqplw0ULCaX2xC+wHdzP6eFPYahfn/DnXwRNR/q4f5fpHEH39EJfu7aPI/Vu8oJljP8+mZEP9yA+ug5TF63g8XfGseC9l4kMD/Wa3ppnY+xX3xMRFuKHaJ30YWFcOetrcvbuY3+/AZjiYqn/+mg0nY6jYz8oNW3Eww8SO+Ytjv/fBI68NQZ9eBihbW9EM1RNUdNu7IJ2UzfUz3NRp0+gu74juoefxTHpDchK95ywXiN09w5BbVqJWjoTrWFztJ4DUbnZkLLLpzHrw0Jp9M0Mcvfu42D/gZhiY4l+dRTodJx4/0Ov6Y++/Q+yNm4q3M5Pq5qL16QZsxk/7RtGDulPQmx9ps76jseee4WFU8cTWaumx3SZWVk88sxILGYzo4cNITwslAOHDmPLz/d5zPqwMJoUlP19BWW/weujoQxlP7JY2TdUcdmfPH8p479bwsjedxMfE8XUH3/i8TGfsuD9UUSGh3lNb82zMXb6t36tewgOwfLeBByHD5L92nPoousR+MRzoNNh/eL/PCbTgoJxnDhK3rIfUGdOoYuKwfTIIPRXNCXryUfAYfdL+FpIKBETnNeus88+hb5+fcKec167Mv6vbNcuS88qvHYFh2B5bzyOvw6S/XpB/g96FjQN69TxHpM58/8Yect+dOZ/3WhMfQahb9yErKce9Vv+XzB52QYmJK9nRI9bia9Ti2krfqP/uJnMf7k/kaHBHtNl5Vq5+/okGkZFoNd0LN26h+enzkenaXS6ponP445ObEJS1ztI2bARvdFY5nQDZ02lzpWN+GrAMyiHg55j32LwvK/5sF1nH0br2aSp0/m/z6fywtCnSIiL5YsZM+n35HB+mDWdyIhaHtPN+mJiiX2Dn32Rllc392W44jJTLRsuwb0eRAs0cfq5Z1BZWVg3gC4omNDBT5MxdQoqK6vU9FpIKGFPD+f8Jx9S840xfoq6JGuejckLljOwRwd6d2oHQIvGcXQY9gYzlq5m+P13ej3H5z/8RJ2aYdSvE8G+I8d9HTIAtR/tgy4wkP39B+HIzITVa9AHBxM94lmOj5vg3OeGoWYNGrz5Gn+Nfp3TM74p3H9ucbJf4i5Bb0C7oTNq/WLU7ysAcBw9gO6pd9GuvQ21ap7HpLqb7oTD+1BLZwKg/voTLTIa3c134vBxw6VWnz5opkBSBg3GkZlJ5pq16IODiXpuOCfHT/SY/xdYDxwke8sWn8bojdWax+QZsxnU53763HsXAC0Sm3L7/X2Z8d1Chg/s6zHtxOmzyLPZmDv5EwJNJgDatLzaL3FfKPv7ipX9mHKU/VMXlf20Kir71jwbk+cvZeDdd9C78y0AtGgcT4dnXmNG8mqGP9Dd6zk+X7icOjXCnXXP3/6pe4zd70Mzmch+YwRkZ2Hf/CuaJRjTo4OwzpoG2e7rfvuubdh3bSva3roJx6lUgt4bjy6hMY79e/wSf1CvB9FMJs4+77x28avz2hXyxNNkTivbtSv06eGkf/IhNV73/7XLeOd9aEYT2W+OvCj/gzA98gTW2V+WPf+3bcJx6iRBY//Pr/kPYLXlM2X5BgZ2aEPvdq0AaBEXTcc3xvP16s0Mu7Odx7Qv3ePao9u2aTz7j59mwcYdfmm4bFu4mK0LFgEwaM6XBJdyk39BfJvrSOzcgQ/adWb/mvUAnDt6nJd+W0GT229hz08rfRlyCVarlUlTv2JQv0fo88B9ALS4Konbut/HV7O/5dknB3lM26J5ksv2th27SDt3jjs7+b6nXVw+quUAu8CbbiZ3/VqXSj57ySJ0ZjOma6/zmj7s6WFY/9hM7q8bfBmmV1v2pZCZk0uX61sW7rMEmri1ZRJr/vB+83vs9Fk+/+EnXn70Xl+GWULYbbdwfuUql5u0s/MXoDebCbmhjcd0Nbo7G2JnZs/1dYhlU68RWqDFdXiXLQ+1fytawyTP6fQGiLuyxLAwtWsjxDQEk9lHATuF3tqejNWrXfI/bcFCdGYzwW2u9+nPrixbduwiMyubLrcW3SRYzIHc2vZ6Vv9a+nC77xYv495unQobLf5UWtkPLaXs1ywo+6erSdnfsvegs+5pU6zuaZXEmj92ek1/7PRZPl+4jJf73efLMEswtG5L/u+/uNwg21YmowWaMVzVqlznUunnAdACAio1xtIEtr2Z3F9cr105yQXXrlber12hTw4j74/NWKvo2mVofWPJ/F+xFC0wEMNVLUtJWVJR/vv3+eiWlL/JzLXS+aLhXRaTkVuSGrFm14Fyny88yIwt3z89RkqpcqdJ6tKR8ydSCxstAIc2buLUwRQSu3SszPDKZPO2HWRmZdGlY9HrOyxmM7fe3JY168tXrn9cutyZtt1NlR1m9aDTqve/aqpaNlwC4hOwpaS47LOfOI4jJ5uAuPjS0za+gqAePTn/0Xu+DLFMDh5LRa/TEVs30mV/QnQUKcdPek3/3lfz6NzmGhLjyz4mvTIENmpI7n7XCj7v6DHs2dmYG3menxDc8hpyDxwk4qEHuHrTr7Q6fICmP84n+Nry3XBUFi0iCuWww9lU12+cPg61ojwnrBGJpjegTrs+ZVanj6PpdM45Lz5katgQa7H8tx1z5r+poff5IQ0+fJ+rDx0g8fffiH5tNFqg/xsABw//jV6vI7ZetMv+hrENSDnseX7R38dOcCbtHKHBQQwa+SrNb+vODd0f4J+fTSLPZvN12JhLKfuBZSj7kQ89QItNv3Lt4QM0q8KyX1T3uA45SoiJIuVYqodURd6b/h2d27QkMb6Br0J0S1c/DseRQy771MkTqJwcdA3ivJ9A08BgQFcvlsABz2DfswP7nh0+idUdQ1wC+R6uXQYv1y5D4yuw9OjJ+Y+r7trlNv9PFeR//TjvJ3Cb/94bypUpJfUsep1GbGQNl/0JURGknCzbsNl8u4P07FwWbtzJuj0p3H/TNb4ItVJENWlM6p69Jfaf2L2XqCalzynxhYOH/kKv1xNX33Ueb8P4WA4e+qvM51FKseSnFdzW/ibMgYGVHaa4jFXLoWK6kFAcGSXnHzjS09GFlj42O/ylV8mc+TX5Rw6jj47xVYhlkp6VjSXQhF7n2j4MDTaTY80jLz8fo4fx77/u3Mva7btZ/OGr/gjVhT4sDHt6yfy3nzuPvpSx8QG1IwlsmED08KEc+cc75KelUffJwVzx9Zdsu7E9+af9PEkz0AJ5Vij+FCs3G81oAp3e/djrQIvzf2uxCdW5Wa7f9xGDp/w/fx5DKfnvyLNyauo0MlavwZGRSfANbaj95GBMsbGk9B/oy5BLSM/IwGI2o9frXfaHhgSTk2slz2bD6OZJ+KmzaQC8P/4/dLu9PZPff5s9B1L4eNJUDHo9I4f092nc+rAw8j2U/dLy3lPZv/LrL9laBWXfY90TZClb3bNtN4s/fs0fobrQQkJQmRkl9qvMdLRg73MCLe98iqH1jQDY/9xF9qhnSn7+fcjTtUuV5dr14qtkzfoa+5HD6OtWzbVLCwn1nP8hZcj/MZ8U5f/eXWSPGurX/Ac4n52LxWQsUfbDzIHk5NnIy7djNOg9pIatKUd56OPpABh0Okb16kiHq/zfACgrS41wss+dL7E/O+0cEQlxfo8nPd193R8WGkJObq7Hur+437ds5UTqSbrdIcPEhKtSGy6apr2jlHrFX8G4cFfZaVqplaC5c1cC4uI4PXSwDwNzTymF/aJVV7SCN6K662y78Ctobr8L+XY7Y778lsF3dyrTBH6fqED+o+nQBwezf9AQ0lesAiBz4yau3vgLdR7vy9H3vE8svyQuSwsWxOk23jJ2gZZIeyGd7y/EbocMaFqpQwnyT57i6KuvF25nbtiA7fRp6r/zD8zNmpGzyzdzc5RS2O0Xl/2C/93k84X4NQ9vDFbKeZ7G8Q14+4VhALRp1YKs7GwmfjWLpx/r7funb5dY9s8XlP2MjZto4Yey77HucZPFZap7ps5hcM/OZZrA7xMVyf8CuZ+NRQsJQxfTAGPv/lje+YysYY+BLc8HgXpQkWtXp64YYuM4M8z/164yKWv+j3uvIP/rY3x4AJYxn5I1/HGf5b+z7BfFVVT3uDkW5XKMJ42jI5k9oi/pOVZW79zPmDnLCA400a1Vs0qK2gfc/G20Mv7NLu3HKuz2ood/RXWPu7ofj99z54fkZYSFhnDTDZfH8OgKkeWQK8Rbj0tnoNwNF03TBgGDACZOnEiXcqZ3ZKSjc/N0Rxcc7PZpFgAGA+HPjiT9iymg06GFhKALCnLG8//t3Xd4FNX6wPHv2fRKSyAQWgJIVxBBFAULHVQsIIgKKiDgtYOKcr2Wq1d/V9Fro4hSRFS4SlO6gCBFkF6kd4RQ0/vu+f0xk2Q32c0mIbtZvO/nefIkMzszeffsmTN75pQJCUGFhqFdDCwsD5v+OMigf36cv9y2aUO6t29NWmYWVpvN4e5PSloGIUGBBLi46zN7xTpS0jLo07EdyWnpAOTkWrHZbCSnpRMSFORy3/JgTUrCL7Jo+vtFRmBNcj0TlzUxEYAUu36sttRU0nbsJPiqRuUfqL26V+H3UMFU0frYPmOMSlBw0YtucAg6O8v1TDeZ6eZ2hVpW8pYzPTu1ba6r9I8oPv2dSfxpoVFxadncYxWXjdt2Mujpgqly27ZqSY9bO5KWkY7VanW485aSmkZIcBABLu72V4owZrBq19pxMH77a6/h4y9ncPzUaRo3KL7LzeUoLu/nFpP2uWbeT3aS90M8nPc37TnAoDcLZqxq27QR3W+4lrQMJ2VPenrxZc/Pa0lJS6dPx+srpOzRKSkQXnQWMxUWjk4r2hJQmO3UCeAE1r27yN25lfAZCwi4vQc5i+d5IFon/9/FtUu5uXZFPjOalKmTQVlQ4RGocO9du+zplGQIKzrrlgoLd9oSU1iR9P9qAQG3dSdnyXwPRAubDh5n8McFk2G0bViH7q2bkpaVXSTvJ2dkERIYQIBf8fk3NCiQFnVrAnBj4/qkZGTx/vxVPltxSb+USHh0VJH1IZUrOW2JKU8bN2/l4eEFU+23u7Y13bvcRlp60bI/OSWFkOBgl2W/vdzcXJauWEXX224pUeuM+N/iLgf5KaWq4OIWtdbaaYdRrfUkYFLe4olPx5UqqJwjh/GPi3cMpEYMltAwco4ecbqPCgnBP6YmVUaPocroMQ6vRf3fB+QcP8aZO7qVKo7SaB5Xh9n/HJW/HBYcRMKlJKw2G8fPnCOuVsG4iMOnE4ir6Xq6yyOnz3LmYiI3jxhb5LXrh77EuyMf4s6b2pbvG7CTefBQkf78gbVq4hcWRsZB14MbMw4cRNtsRXKLUgpsHm6lOHMM65f/LFjOyoSIKlgsflCluuM4l2o14cIZ18e6dA5tzUVVi0EfL+g7rKrFGO+v8JiZcpZ1qGj6B9Q00j/rUCkHl+ZV2DyY/M0bN2T2pIIvzmGhoZw9dx6r1caxU6eJr1vQ1/nwsRPE1XU9ZqtObE0CnAzmzXsbFotn71BlHDxUZBxXXt4vPPbFXqaLvI8X8n7z+LrMfuuF/OWwkGASLiY6L3tOJTgsF5Zf9gwfU+S16x8bzbtPDOLOm90PMi8r24mj+BUaS6Gia6BCQrEdP1qqY+mzp9EpSVi82O0q96jra1euq2tXsHHtqjxqDJVHOaZ71Xc/IPf4MRLu8ty1y57txFH8Co0lyk//QmNf3NFnz6BTkj2a/s3rxDBrVMEMhWFBgSQkpWC1aY6fu0RcjYJZuY4kXCCuuutp2F1pVieGOb/tJMdqdVvpqQhn9h7gpptvLLI+pslVbJv7o0f/d/Omjfnv9Mn5y2GhoSScO4/VauXYiZPE16+X/9rho8cdlouzftNmLl5KpFc3708uIHyfu4pLE2AzrlpeId7J+suW+esaIgY/6nCnKbRbT2wZGWT9vtHpPjo9nbOPPeywzhIVRdS740j8zziyNnl2lpawkGBaxDsOZK0VVZXwkGAW/7aNEXcbF56MrGxWbdlF39uKFjR5BnbtSOfrrnZY9/n8ZZw8d5HXH7uf+FjPDg5PWrGKmBGPYwkLw2bOjlP1zjuwZmSQst51OiYu/5nYUc8SeeONJK0wph/2i4gg9OoWnJkwyeV+5SI7C04XGviXdAGdmY5qeh167U/GOv9AVKOr0VvXuD6WNReO7kM1bYPeujp/tWrWFk4dKjr2pZwlr/yF6sOHOaR/5Tt7Gw803PBbqY5VuVdPANJ37Cz3OPOEh4bSstAg0Nga1QkPC2XJyjWMGDQAgIzMTFau+41+d7hugw0MCODG667lty3bHdav37yNkOAg6sXWLP83YCdpxSpqusj7yWXI+2FeyPthIcG0aOD4hSC/7NmwhRH3GOltlD076Xu76xl6BnZzVvYs5eTZC7w+ZADxscVMalEOcjetJajvwxASChlGi0/ALV3RmRnk7tjsZm9Hltr1sFSqgu30KU+E6lTm2jVEPOx47Qrpal67Nru4dmWkc26I47XLLyqKqu+MI+njcWRt9N4MY7mb1hHU9yHH9O/UFZ2ZSe6OLaU6lpH+lbGd+dMToQLGDcK81pE8tapWIjw4iCXb9jK8m/FAw4zsHFbtOkjfG1uV+n9sPXySmMoRPllpAdi1aBm9Xn2RBh3ac2itkVfqtmlNdIM4di9a5tH/HR4WRstmjg/njK0ZQ3hYGIuXr2TkkMGAWfav+ZV+d99VouP+tGQZ0dWqcX0b350UoVyUsNuccOSu4rJHa+31nJM6+1siHniQqHEfkTxlMv616xA54glSvprqMM1kzIIlZG3exKXXxoLVWqRSkzc4P+fgfrJ37sDbggIDGHpnZ8bPWUJkWAjx5gMobTbNg90Kpomdu3ojYyfNZMkHrxIbXZV6MdHUi3GciWzO6t+4lJJGu2Ye7nIFnJ0+g+qPPULDLyZx5tPxBNWrS61Rz5Iw8XOHaWJbrltNyvoNHH3euNubvn0HlxYvof64f3PyrXfIvXiRmCeGo3NzOTtlusfjLsKai16/GHVTL8hMQ18wHkCJUujff87fTLW8AdV7ELZPX4ZkoxHR9uuPWB4ahepyP3rfVlTDltCwBbZvSvYQuctxYcYMoh8ZTNykCSSMn0BQ3brEPPsMZz+f7JD+TdesInXDb5wYbXTTinn2GSzhYaRt+h1rairh17ej+vDHSVy4iMy93nuOAkBQUCBDB/Zj/LRviIwIJ75ebaZ+Nweb1vnPdQGYu3g5r7z7AUu/+ZLYGKNC/sSgBxj4t1GM+dc4et3eiX2HjvD5zFmMfHgAgaV4KFtZnJ0+gxqPPUKjLyZx2sz7saOe5UyhvH+1mfePmHk/zcz7cWbez7l4kZpm3k+ogLwfFBjA0Lu6Mv6HRUSGhZplzwoj/bt1yt9u7urfGDthBkv+8xqx0dWoF1OdejGOrcFzftlglD3NPT9AOXvBfwns05/Q194j67upWGrWJujhx8n+/muHKXrDp80jd8dmMt9/w3i/w54BmxXrH7vQqSlY6sUR1G8QtlMnyFnlvWfppM3+lvD+D1L1/Y9InToZv9g6RAx/gtQZjteuGvOWkLVlE4mvG9eu7EKVmrzB+bkH9pOzy3vXruwfzfT/x7/J+m4alpqxBD08jOzvZzim/9S5RvqPexMw099qxbrXTP+6cQT1e9jr6Q8QFODPkM7tmbBkLZEhweYDKDdh0zr/uS4A8zbuZOzMhSx+dTixVStx6mISY7/+iV5tmlM7qhLpWTks37GfhVv+4NV+3mnxCggJoWXPrgBUjq1FcGQE195rfNnfuXApORkZvHFgGwd++ZWvhvwNgCMbNrJ78XIGT5/I96PG5j+A8sCadV5/hgtAUFAQwwY/yGeTp1IpMiL/AZQ2m+ah+wumV5/74yJefvNfLJs7i9iaBTdEsrOzWb5qDXff0cPjLeziyuSTs4rplGTODnuEKmP+TtRH49EpyaTMmEby+E8ctlN+/iiLb94FyTP0zi7YtObz+ctITEmnRXwdvnh5JFGVCvpBFwyu9e7sK65Yk5LY128A9d56g0bTviQ3OZmESZM59d4HDtspfz9UobtQh594mjqvvkLd1/6OJSSElE2/s+++/liTPNvX1hW9bhEohbqxJyokDE4fwzbzA7DvL6+UkY/s736cPIjt+wlYOvVBXdsJEs+j504GDz98EsCalMzBAQ9Q+803iJ/yBdakZM5N/oIz4z502E75+Tukf+ahQ1R/fCjV+t+PCg4m588/OTthIgkff+rxmJ0ZNrAf2mZj0tffkZiUQosmjfjy/beIqlowTanNHNhvPwTp6maN+eyd1/hg4hR+XL6SapUrM/yh/gx78H6Px2xNSmJvvwHUf+sNrjLz/hkXeZ9Cef+Qk7y/twLz/tC7uhplz7ylJKak0SK+Ll+8/CRRdhN+aJvNKHt8o+iB1BTSRw8n+MmXCH3zQ3RqCtnff03W9EJP1PbzA7svNdb9ewjs05/AnvdAYCC2s2fIWfMz2d9MgcxMr4WvU5I5P/wRKr/4d6p9OB5bSjKpX08jZYLjtQt/f2NWQ1+TmkL6C8MJ/tuLhL75ATo1lezvZ5L1lZP0t8v/RvrfT2DPuyEwyEj/X1d4Pf3zDO3S3sj7yzaQmJ5B8zoxTB55P1GRYfnb2PIG9puFT2RIENGVIpiwdC3nk9OICAmmYUw1xj/el07N3U9DXx4iq0cz7L9fOazLW36lfgsuHDuOn78flkJlz+T+j9D3g3/x8JefoiwWdv64hO+eGk1FGTb4IWw2zcSpX5GYlESLpk2Y8umHRFUr6Kpn0zasVmuRCWdWr9tASmqqzCYmXFLFzVKklBqstZ7qZH0wcIfWenYJ/oc+cY3nnzjrCXW2G3epbZsr6Mnvl95Z7cIAACAASURBVMnSxrhLtKmmd5/FUF7anj6O9S3vTuNbnvxe+ZxtJXn2gY9qdeIoOuFwRYdRJqqG0Yt14xWa99udPg6AbevyCo6kbCytjS8dyZ1L99BCXxG5fAunvPCkdE+J3bqX5C4V8wyh8hC5bDPWJVMqOowy8ev2CADDVQXNCHqZJmhzEokULz++oLxEREGJpw6tWLZdq33llpFTlhYdfTIdi22Hs6+0KKX8lFI9lFLTgWOA529/CiGEEEIIIQQl6CqmlOoIPAD0AjYCHYA4rXW6h2MTQgghhBBCCMD9AyhPAseB8cBorXWKUuqIVFqEEEIIIYQoI5lVrEzcTdnwPRCL0S3sDqVUGL4zjFMIIYQQQgjxP8LdGJengfrAOOBWYD8QrZTqp5Qq+nhdIYQQQgghhPAAt5Nka8MKrfVQjErMA0Af4KhnQxNCCCGEEEIIg7sxLlO11oPzlrXWOcACYIFSKsTDsQkhhBBCCPHXo+QBm2XhLtWudvWC1jqjnGMRQgghhBBCCKfcTYccqpRqjYuH+Witt5R/SEIIIYQQQgjhyF3FJRZ4H+cVFw3cVu4RCSGEEEII8Vdmka5iZeGu4nJQay2VEyGEEEIIIUSFkuqeEEIIIYQQwue5a3F50X5BKRUAtABOaa3PeiwqIYQQQggh/qKUcjp8XLjhrsXlHqVUcwClVCVgOzAd2KqUGuDp4IQQQgghhBAC3FdcbtZa7zb/fgTYr7VuCbQBXvBoZEIIIYQQQghhctdVLNvu7y7AbACt9Rlp4hJCCCGEEKIM5AGUZeIu1RKVUr3NZ7l0ABYDKKX8gRBPByeEEEIIIYQQAEpr7fpFpa4CPgJigA+11lPN9d2Arlrr50vwP1z/AyGEEEIIIcrPFdElSO/f6NPfj9VV7XwyHYvtKqa13g90d7J+iVKqqceiEkIIIYQQ4q9KhlyUibsxLsV5DviwJBtaF39xGf+m4vh1fwyApFuuqeBIyqbSqu0A2LYsreBIysZybVeyh3Sr6DDKLHDyEqyrvq3oMMrM75b+fF81pqLDKJN7L54BwLp0asUGUkZ+XQcDkDOiZ8UGUkYB4xcCYJ09roIjKRu/vs9hXTO7osMoM7+b+2Lbs7aiwygzS7MOpN7RvqLDKJPwBRuMP1LOV2wgZRURBcBwFVnBgZTNBJ1c0SEID7uckUFSVRRCCCGEEEJ4xeW0uPh03zwhhBBCCCF8kswqVibFVlyUUik4r6AoZFYxIYQQQgghhJe4G5wf4a1AhBBCCCGEEMIVaacSQgghhBBC+LzLGeMihBBCCCGEKC2ZDrlMpMVFCCGEEEII4fOk4iKEEEIIIYTwedJVTAghhBBCCG+ySNtBWUiqCSGEEEIIIXyeVFyEEEIIIYQQPk+6igkhhBBCCOFNMqtYmUiLixBCCCGEEMLnScVFCCGEEEII4fOkq5gQQgghhBDepKTtoCx8vuKitWbSsg18t3Ybl9IyaFE3hpfvuZ2mtWsUu9/HC39l+Y79/HkxGY0mrnpVHr2tHT2ubeqVuC314gl56iX8ml+NTk0h+6c5ZE2bADab633qNyB45PP4xTdCRVZGX7pA7u/ryfziU/TF816JuzCtNZPmLeXbZb9yKSWNFg3q8sqg+2hav3aJj7F803aeHDeZ5nF1+O/bL3gwWjs16+L/wEhUfFPISMO2ZjHW+TNAu05/Vf8qLLfegaVRC6hUFS6dw/rbSmyLZkFujnfiLkRrzaRFa/hu9SYupabTol4sL/fvQdM6NYvd7+P5K1i+9Q/+vJiE1pq4GlE82rUDPdq28HjMEY2votU7b1G1bRtykpI5OmMme959r9i83/TFUTR7cZTT13a98Rb7PvzYU+G6pLVm0tL1fPfrFrPsqcnL93VxX/b8tJrl2/fz56UktMYoe26/nh5tmnkn8Jg6+N0/AhXfBNLTsK1dgu2nmcXmffz8sdz1MCquCapuI1RgEDkjenonXhe01kz6ZSvfbdzDpfRMWsRG83LvDjStGeVyH6vNxpRfd7Bq3zEOnb0EQPNa0TzdpS0ta1f3VuiAGf/CX/hu1Ubj3K0fy8sDetO0rptzd97PLN+yhz8vJBrnbkwUj3a7mR7tWnopcoPWmknf/8S3i1dyKSWVFg3jeGXIAzSNq1vsfmM++oK5K9cWWf/Tx28RX7v49365VJ36BD3+PH5NWqJTU8hdNp/sb74o/rpbN47AR5/CUr8hKrISOvEi1q0byZ4xEX3pgkfjdUVrzcQp0/nm+7lcSkykZbOmjB31DE0bX1Xsfo2v6+B0fUBAALvWr/JApI6iG8TTZfRTxLdvS60WzTi4Zh3jbu3ldr/gyEj6ffgO1/TphcViYeePS/juqRdIu3jR4zGLK4/PV1w+X/4bE5auZ9SdtxBXoyrTVv7OY5/NYt5LjxAdGe5yv7TMLPq0a0GDmCj8LIql2/bx/LQFWCwWurVq7NmgwyMIe38itmOHSX/lGSyxdQge8TxYFFlffOpyNxUWju30KXKWLMB24RyWmFiCBz9O2FXNSB3+AFitno3bic/nLWP8D0sYPfAu4mrVYOrClTz61ifM//fLRFeOdLt/VnYO786YQ1SlCC9EawoNJ+D5d9B/Hif309dR0TXx6zcMlMI6d5rL3SxtO6Gia2JdNAt99hSqdhx+dw3CUjue3PFvei9+O58vXsOEhb8w6t6uxMVEMW35eh77YDrz/jGS6GLSNC0ziz43tqJBzepG/t+8h+cnz8ZiUXRr09xj8QZUqsTNP8wiZd9+1j84mLD69bn6zddAKfa8/a7L/Y5+9TUJP69wWFerZw8aP/MkZ5avcLGXZ32+bD0Tlqxl1F23ElejGtNWbuSxT75h3stD3JQ92fS5vqVj2TN1nlH2tG7i2aBDw/F/+m30meNYx7+Jiq6J5d4hYLFgmz/d9X6BQVg6dEMf3Y8+/AeqSSvPxlkCn6/exoRVWxjVvT1xUZWZtm4Hj035iXlP9iU6ItTpPpk5Viav3srd1zZmaMfWKAUzN+zmwc/nMXNYH5rHRnsv/kWrmfDjKkb17W6cu0vX8di4Kcx7/cniz92MLPrc2JoGtfLO3d08P+k749y9zvM3HvLj/2Eh42cvYPSgfsTFxjB1/lIe/cd7zP/Pm0RXqVTsvvGxNXnryUcd1sVWd13hLBdhEYS8+TG2E0fJ/OcLqJhYgh57CpSF7BkTXe8XGo5OOE32ykXoC+dRNWoSOGAIwQ0ak/Hco2Dz/nV30tSv+OyLqbzw1BPE16/HlK+/ZfDIZ/jxu6+Ijqrmcr/vphR9n8OffZFrr/FOpbdW8ya06NmVIxs24RcYWOL9hn43lRqNGzJjyJNom427332D4XNn8n7H7h6MVlypfLrikpWTy+TlGxja+XoGdrwWgFb1a9Hl9YnMXLOVp3vd7HLfl+653WG5Q5M4Dp65wPxNuzxecQm6sy8qKJi0vz8H6WmweQOEhhE8eDhZ30w11jlh3b0d6+7tBcv8jj6XQNj7E7HEN8J2YK9H4y4sKzuHz+cvY+hdXRjYrRMArRrF0fmp1/h6yWqeub+322N88ePP1KhSiTo1ojhw4rSnQwbA0qkXBASS+9kbkJmOBggJxe+OB7Eung2Z6U73sy6aBalJ+ct63w7Iycb/4WeganW4eNYr8efJyslh8uJfGdr9Zgbeej0AreLr0OXlD5i5ciNP97nd5b4v9evhsNyhWUMOnj7L/A3bPVpxiX/kYfxCglk/6FFyU1KB1QRERND0xefZ//Gn5rqiMv48TcafjvmjyajnSN6/n6Rduz0WrytZOblMXraBoV1uYGCn6wBoFRdLl9c+Y+bqzTzdu5PLfV+6t7PDcoem8Rw8fY75G3d6vOJiubknBAZinfhPyMxA790KwaFYej+AbelsyMxwvmNGGrnP328co1NvqOCKS1ZOLpNXb2Nox9YMbG98WW9VtwZd3pvJzA27eLpLO6f7BQf4seT5B6gUEpS/rn18LD0//JavN+zi7Xtv9VL8OUxetJqhPTsy8Lb2RvwN6tLlxfeYuWIDT9/dxeW+L/V3bOnq0LwRB0+dZf76bV6ruGRl5/D5DwsZek8vBvY0yplWjRvS+fHRfL3wZ54ZeE+x+4cEB9KqcQNvhJovoMfdqKAgMt9+ETKMMl6FhhH4wBCyv/8qf11htr07ydq7s2DFLtAXzhHy5kdY4hpiO7TPG+Hny8rKYtLUGQwb/BAP3n8fAK2ubsFtd9zHjFnf8+zIYS73bdXSMX/s2LWHS4mJ9O7W2cUe5WvHgkVsn78QgGGzpxNeTCUrT1z7djTv3pn3Onbn4Jp1ACSeOs1LG1fS5PZb2PvzKk+GXLFkVrEy8ekOdluPnCI1M5vudhf70KBAbmnRkDV7Dpf6eJXDgsnJLaa7RDnxv/4mcjatc6ig5KxYjAoOwf+a60p1LFtyIgAqIKBcYyyJrfsPk5qRSY/2rfPXhQYHceu1LVizfY/b/f88f5EvFixnzKD7PBlmEZaWbbHt3uxQQbFtXIUKCkY1LubOk12lJY8+fggAFVm53ON0Z+uhE6RmZtH9uoKKRmhQILdc3Zg1uw+U+niVw0LJyfXs3cManW8jYcUqhwrKiR/m4h8aSvSNN5T4OAGVK1Pjlo6c+H6uJ8J0a+vhk0baty7oWmqUPY3KWPaEkOOFFlPV/Dr0ni0OFRTb77+gAoNRjbzb1ehybD2eQGpWNt1bxuevCw0M4JYm9Vhz4ITL/fwsFodKC0Cgvx8Nq1flYlqmx+ItbOvB46RmZNH9uoI0Dw0K5JZrmrBmZxnO3XDPn7v2tu49SGp6Bj06tM1fFxocxK3XtWLNlp3F7Flx/NvcQO6W3xwqKLlrlqGCgvFrcW2pjqVTzGuBv/fv7W7ZsYvUtDR6dCm4MRUaEsKtN3dgzboNpTrWT0uXG/t2vKm8w3RKa13qfVr06ELSmYT8SgvA0U2bOXf4CM17uK7gi/9dPl1xOZJwET+Lol50FYf18TWqcuRsyfo+5lptJKdnsuD33azde5R+HTx/J9FSNw7b8SMO6/TZM+iMDCx167s/gFLg74+lTj2Chz1D7h+7sP6xyzPBFuPwnwn4WSzUq+nYNzw+tgZH/kxwu///zZhD9/ataR5Xx1MhOqVi6sCZQl9uLp5DZ2Uar5XmWA2aoW1W9JmT5RhhyRw5c95I/+qOd63iY6I4cqZkY55yrVaS0zNY8NsO1u45RL9Opas4l1ZEo0akHDjosC7j1Cly09KJuKpRiY9T+67eWAIDOflDxVRcjiRcMMqe6oXLnmocSShZv/f8smfTLtbuPUK/DqX78lQWKqZ20bx6qWx5vyIdOZ9opH81xy5J8dGVOXIusVTHys61svvPczSo7r2bD/nnbo1C527NaI6cOVeiY+Sfuxu2sXb3Qfrd0tb9TuXk8KnTZtnvOJ4rvnZNjpxy33J+6MRprntgJFf3HcbAMW+zcZfnWy1U7Xrok8cc1ulzCejMDCy165XgAMZ1V8XWJXDQSKz7d2Pb7/4GXXk7fPQYfn5+1K/jOI60QVw9Dh895mKvorTWLP55Jbd1uomQ4ODyDrPcxDRpRMLe/UXWn/ljPzFNih/TI/43+XRXsaSMTEKDAvGzONavKoUGk5GdQ3aulUB/P5f7bz/6JwM+mAGAv8XCK/d1pvPVJf/yVFYqIgKdmlJkvU5NRkW4HxcS+u6nBLQzBtnl7ttN+ot/gzLcybhcyWkZhAYHFUn/yLBQMrKyyc7NJdDFHanfdu/n1x17WTTu794I1VFoONpZd7y0FFRoKcbaRFbBr9cAbOt/dtm9zJOS0jOc5/+wEDP/u05/gO2HTzDg3cmAmf8H9KRzK89OThFYuRI5SUVbrrKTEgmoVHy/eHu17+7DpW3bST1U+taN8nDZZc+RUwwYZ4wp8bdYeKVvVzpf44WLcGg4ZDjpjpeearx2hUjKyCI0MKBo+ocEkZGT6zb97U1ctYXkjCzua+OdiVkAktJcnLuhJTx3D51gwL+M8Qr+fhZeeaA3nVt7aXIHIDk1zSj7/QqV/eFhRtmfk0tggPP4m8bV5eqr4mlQuxaXklOYMm8JQ15/jxlvjeHqq+Kd7lMeVHgkOs3ZdTcFFe6+3A/+xzj82xitwtYDf5D5+nMVc91NTiE0JAQ/P8f8XSkygozMTLJzcggsQQ+M37du50zCWXp19U43sbIKrVKZ9MSi14z0S4lExdf3fkDC5xVbcVFKNdFa7zX/DtJaZ9m91l5rXbp2y2JorbHaCgqJvK5/znoA5pUl7roHNqoZxaznHyI5I4vVuw/x1n+XEx4cSC9vzO7jtMBTJSoIM//zDlmRlbDUrkvQQ0MJe/dTUp8cBNnZ5R+nyUj/gm50ykxcZ2mc1xysnH46xp3Ct6b9l+F9upVoAL9HOEtnpYASXoj8/PEf/gpkZWD9rpiBneWkTOnv5gRoFFuDWWOGkZyRyeqd+3nrm4WEBwfTy9OzEzlJe1XCvA8QXKM60R1uYOfr/yzvyJxyXfYUTd8Slz21opk1ejDJ6ZlG2TN7qVH2XOe58UUFQTpZp6iQL2El4YmyP88v+44x8ZetvNCjPXHRnmlxKdW5S/FlZ55GtWswa+wIktMzWL1jP2/N/JHw4CB6XX9N+QWeF1Ph+MmL31n+zyt7XB/v4Tscu/d0anM1vZ8ay6Tvf+KTMU+WQ8TFcFrul2zXrInjyI6IxFKrDoH3Dyb4tQ/IeGEY5Hj4umvXhbQg7xRX9pTsDf24ZBmVIiO46YbrLz9QT3N2zVAlv2ZcuWSMS1m4a3GZCeT1b1hv9zfAZ4WW8ymlhgHDACZOnMhjdd3fGdt08ASDP/k2f7ltwzp0b9WEtKxsrDabw52r5IxMQgIDCPAr/rihQYG0MKeevLFxfVIys3l//i8er7joFOd3eFR4uNOWmMJsp47DKbD+sZPcHVuI+GYhAbf3JGeR57rNbPrjIIPe/Ch/uW3ThnRv35q0jKwi6Z+SnkFIUCABLu54zl6xjpS0DPp0bEdymtFSkZNrxWbTJKelExIU5HLfcpGeCqFhRdeHhKHTnQ8OL8zvsdGoWvXIeec543getmn/UQaPm5q/3Paq+nRv05y0TCf5P70U+b9+LAA3Nm1ASkYW7/+wzKMVl+zEJKctKwGRkeQkJ5foGLX73AlKcXLOvPIOz6lNB48z+KOZ+cttG9ale+smpGUVzftlKnuaxJGSmcX781d5vuLiKu8Hh0GG80lBKtqmI6cZ/OWC/OW29WvSvWUD0rJziqZ/ZhYhAf5u0x9g58mzPPfdcvq1bcrDN17tkdgBNu07wuD3vsxfbntVfbq3bVn8ueum/HM4d5s1JCUjk/e/X+qRisum3fsY9Pf/K4i/eWO6d2hLWkYmVqvNodUlJS3dLPtL3lkjOCiQjte2ZNXv291vfBl0ajKEObnuhpbsuqtPn0CfBtv+3Vh3byN08g/4d+pK7vIfPREuABs3b+Xh4QWVuXbXtqZ7l9tIS0/HarU6tLokp6QQEhxcorTPzc1l6YpVdL3tlhK1zlSk9EuJhEcXnXEupHIlpy0xQrg7A5SLv50t59NaTwIm5S1aF3/hNpDmdWow6/mH8pfDggJJSErFatMcP3eJOLu+wkcSLhJXvarbYxbWrHYN5vy2kxyrtUQXvrKyHT+CpW6cwzoVXQMVEort+NFSHUsnnEanJGGpFVuOERbVPK4Os/85On85LCSIhIuJWG02jp85R1ytgr7Oh/9McFgu7MifZzlzMZGbR7xS5LXrh7zIuyMf5s6bPddfW585UbQ/f5VoVHAIuvDYFyf87h+OpdUN5I4bU3SsjIc0r1eLWWMKZosJCw4iITHZSP+zF4mLKSjYjyScd1guqWZ1azJn3VaP5v+UAweIaNTQYV1IbC38w8NI2V+yQcm17+nD+Q2/kXHqT0+EWETzOjHMGj04fzksKJCExBQXZc8Fh+WSala7BnM27PB42aPPnETVKPSMpSpRJc77FaF5bBSzRhTMUhUWGEBCcpqR/heSHVpKjpxLLFHLydHziYz4ahHt42N5pbfzZ1uUl+b1Y5k1dkT+clhwIAmX8s7dC8TFFEzBfOTMOYflkmpWrxZz1m4hJ9da7jd9mjeox+x/F3TpDQsJJuFCXtmfQFxswbNXDp867bBcKh6eQUmfPFZkLIuKqm5cdwuNfXF7rHNnIDUZS4yHr7tNG/Pf6ZPzl8NCQ0k4dx6r1cqxEyeJr1/wfg4fPe6wXJz1mzZz8VIivbr5/uD2M3sPcNPNNxZZH9PkKrbN9VylUVy53FVctIu/nS1flrDgoPw7lHlqVa1EeHAgS7btY3g3I2NnZOewavch+t5Y+jtPW4+cJKZyhEe/OADk/vYrgf0HQ0ho/gwnAbd1Q2dmkLv991Idy1KnHpZKVbCd9uyXuLCQYFo0cHywWK2oqoSHBLN4w1ZG3GPMp56Rlc2qLbvoe5vrLwMDu3Wkc1vHO5yfz1vGyXMXeH1If+KLqfSUB9vOTfh174s1KASyjNmVLG07obMy0fuKnxHH0uN+LLffSe6Et9EHvTcNb1hwUP4d1jy1qlUiPDiIJZt3M7yXMf1uRnY2q3bsp+9NbUr9P7YeOk5MlUiP5v+E5Su46smR+IeHkZtq3OGvffdd5Kanc27derf7h9apQ7W217H1+Rc9FmNhrsueIJZs3cvw7kZez8jOYdWug/QtwwQfWw97p+zRu3/H0uVesM/7bTqiszPRB3xzNqiwoEBaFHq+Sq3K4YQHBbJk12GG32o07Gdk57Bq3zH6Xlf8WJVzKWkMnbqQOlUjea/f7UXGmZQ35+duZcJDgljy+y6G9zamYM7IymbV9n307Vj6CTK2HjxmnLseaKkOCwmhRUPHG221oqMIDw1h8brfGdH3DgAysrJYtWk7fbu6ngrcmcysbNZs3Unz+JJ96S6r3M3rCbxnIFl2113/mzujszKx7tpSqmOp2LqoyMrYEjx73Q0PC6NlM8f8HFszhvCwMBYvX8nIIYMByMjMZOWaX+l3910lOu5PS5YRXa0a17dp7X7jCrZr0TJ6vfoiDTq059BaY/RB3TatiW4Qx+5Fyyo4Og+T6ZDLxF3FpbZS6iOM1pW8vzGXPXsrAggK8GdI5/ZMWLKOyNBg4qpXY9qqTdi0ZuDNBb3U5m3cxdhvFrH478OIrVqJUxeTGDtzEb3aNKV2tcqkZ2WzfMcBFm7Zy6t9PX8HImv+bALvfYDQN8eR9c0ULDVrEzx4BFmzv3KYIjn86wVYt20m49+vARA84jm01Yp1z050agp+9eIIGjAY66nj5KxY7PG4CwsKDGDonV0YP2cxkeGhxNeqwdSfVmCzaR7s1jF/u7mrf2PsxJks+fAfxEZXpV5MNPUK3VWc88sGLqWk0q6Z5ydHsP3yE36334X/E69iXTQLFR2D350PYlv2g8Mg+4C3p2DbtwPrtA8AsLS7Ff97H8W6dikknjeePG7SZ087nS7Zk4ICAhjS/SYm/LTayP8x0Uxbvg6bTTPwtoJ+y/PWb2Ps9Hks/ufTxFarzKkLiYydNpde7VpSO6qKkf+3/sHCTbt49QH3z965HIenTKfBsCG0n/4l+//zCWH169HshVEc+GyiwxTJ3X5fz7l169ny1HMO+9e+5y5sOTmcnF+xd9qCAvwZ0qU9ExavNdK+RjWmrdholD12Xzzn/baTsTN/YvE/RhSUPTN+otd1zYyyJzuH5dv3sXDLH7x6fzePx21bsxDLrXfi9/hY47ktUTFYeg3E9vNchymS/V+fjD6wE+uM/+SvU82vg8AgVB3j+RuqtVFh08cOeP0ZRkEB/gzp2IoJq7YQGRJIXHRlpq3diU3DwBsKnlUxb+t+xs5ZxeJnBxBbJYLMnFyGTVtEcmYWY+/owP6EgtknA/z8aFbLww9BzI8/gCE9OjLhx1VEhoYQVzOaaUvXGvnntoJpweet28rYqXNY/K9nia1WhVMXLjF2yg/0ancNtaPNc3fLHhZu3MmrD97pldjBLPvv6cn4WQuIDAslvnZNps5fik1rHuxVME3v3JVrGfvJFJaMf4fY6lGkpKUz4q3/cEenG6hbszqXklOZtmApCRcS+WDUiGL+4+XLWTSHgDv6ETzmHXK+/woVU4vAAUPImfuNwxTJoRNnY921layP3wYg8NEnwWrFum+3MYFLnTgC73kQ258nyF3t/S/OQUFBDBv8IJ9NnkqlyIj8B1DabJqH7i94tMDcHxfx8pv/YtncWcTWjMlfn52dzfJVa7j7jh5YPFxpLywgJISWPbsCUDm2FsGREVx7r1HZ2rlwKTkZGbxxYBsHfvmVr4b8DYAjGzaye/FyBk+fyPejxuY/gPLAmnV/7We4iDJzV3EZbfd34aaC0jUdlNHQztdj05rPl20gMT2T5nVimDyyH1GRBf24bXmDO802oMiQYKIjw5mwdD3nk9OICAmmYUw1xg+7l07NvfBQrNQU0p4bRsjTYwh7+yN0agpZs2eQNXW8w2bKzw/s+g9b9+0h8O7+BPa+FxUYiO3sGXJ++ZmsmV+4fnCchw29q4uR/vOWkZiSRov4Onzx8hNE2Q26Lxjc6SMD6dJTyXn/JfwfeAL/J1+H9FRsy37AOn+G43YWi/FjUs2NyrBfh674dejqsGnul+9hW+f9i9jQ7jcb6b/4VxLT0mlerxaTn3mYKLsnt9vy0t8cyBgZGkx0pQgmLFzN+aRUIkKDaVgzmvF/G0inlp6d2SonKYk1d/el1btvc+PM6WQnJXNgwkT2vPOew3bK3x9lKXr3uM49fTi7eg3ZF0o25bAnDe1yg1n2rCcxLcMoe57o76LsMdM+JIjoSuFMWLKO88mpZtkTxfjhfenUvKGrf1V+0lPJ/XAMfv1H4jfiH5CRhm3FXGw/fu24nZ+fQ94H8BvwBKpaQWuo/zCjq2futHHoDcs9HnphQzu2MtJ/9Taj7I+NZvLgXkSFh+Zv5A15FAAAFEFJREFUYys0sP9Cagb7zhh5Z8RXjjd7alUOZ/mogd4JHhjaoyM2m+bzRatJTE2nef1YJj83mKhKzs5dYzkyJIToSpFM+GlVwblbK5rxTz1Ep6s9++DkIvHf0xObzcbnPywkMSWVFg3q88VrzxNVuWAMW+GB/YEBAVSJjGDC7AVcSEohKDCAVo0bMP2fLxZp1Sl3aSlkjP0bQY+PIvjv/0anpZIz71uyv5nsuJ2fv5H/TbYDfxDQux8B3e6CwCD0uTPkrltJ9n+nQZb3nv1jb9jgh7DZNBOnfkViUhItmjZhyqcfElWtoIu8TduwWq1Fnp2yet0GUlJTK2Q2scjq0Qz771cO6/KWX6nfggvHjuPn74elUMvz5P6P0PeDf/Hwl5+iLBZ2/riE754ajRDOqOIeGKSUCgYitNbnCq2vDiRrrUtyVpdojIsv8uv+GABJt5T/gEhvqLTKGAxp27K0giMpG8u1Xcke4vm71J4SOHkJ1lXfut/QR/nd0p/vq8a439AH3XvxDADWpVMrNpAy8us6GICcET2L39BHBYw3np5tnT2ugiMpG7++z2FdM7uiwygzv5v7YtuztqLDKDNLsw6k3tG+osMok/AF5mSrKSV71pbPiTBaJYerCpoR9DJN0MlwhUzXpU/+4SN3e51TtZv6ZDq6a0f8CLjZyfouwAflH44QQgghhBBCFOWu4nKT1vqHwiu11l8DHZ1sL4QQQgghhBDlrjTTIRfm3VFfQgghhBBC/CX4ZE8sn+eu8nFWKdWu8EqlVFvgnJPthRBCCCGEEKLclWRWsVlKqanAZnPddcDDQH8PxiWEEEIIIYQQ+YqtuGitNyqlrgdGAoPN1buB67XW3p3YXwghhBBCiL8CeQBlmbhrcUFrnQD8wwuxCCGEEEIIIYRTxVZclFI7cf5UQQVorfXVHolKCCGEEEIIIey4a3Hp7ZUohBBCCCGE+F8hPcXKxN0Yl2PO1iul/DAG5zt9XQghhBBCCCHKU7HTISulIpVSY5RSnyiluirDk8BhoJ93QhRCCCGEEEL8r3PXVewr4BKwHhiCMT1yIHCX1nqbh2MTQgghhBBCCMB9xSVea90SQCk1GTgP1NVap3g8MiGEEEIIIf6SZJBLWRTbVQzIyftDa20FjkilRQghhBBCCOFt7lpcrlFKJVNQLQyxW9Za60iPRieEEEIIIYQQuJ9VzM9bgQghhBBCCPE/QUlXsbJw9wDKYGA40BDYAXyptc71RmBCCCGEEEIIkUdprV2/qNR3GONc1gA9gGNa66dL+T9c/wMhhBBCCCHKzxXRlKFPH/Dp78eqZiOfTEd3g/Obaa0f1FpPBO4Dbi7D/1Ce/FFKPe7p/yHxS/y++HMlxy7xV/yPxC/x/y/GLvFX/I8X4r8yKOXbPz6qNLOK+WoXsWEVHcBlkvgr1pUc/5UcO0j8FU3ir1hXcvxXcuwg8Ve0Kz1+UQJKqapKqWVKqQPm7youtqurlFqqlPpDKbVHKVW/uOO6q7hco5RKNn9SgKvz/jZnFxNCCCGEEEIIey8BP2utGwE/m8vOTAf+rbVuCrQDzhZ3UJlVTAghhBBCCK/y3e5Y5eQu4Bbz72nAKuBF+w2UUs0Af631MgCtdaq7g7prcbkSTKroAC6TxF+xruT4r+TYQeKvaBJ/xbqS47+SYweJv6Jd6fGLkqmhtT4NYP6u7mSbq4BEpdQPSqmtSql/K6WKbTQpdlYxIYQQQgghRPnSZw759BdwS82Gj+M4HmmS1tqh0qmUWg7EONn9FWCa1rqy3baXtNYO41yUUvcBXwCtgePAd8BCrfUXruIqtquYEEIIIYQQopz58MxdAGYlpdjWMa11Z1evKaUSlFI1tdanlVI1cT525SSwVWt92NxnLtAeozLj1F+hq5gQQgghhBDCd8wHBpl/DwLmOdlmE1BFKRVtLt8G7CnuoD5ZcVFKxSilvlVKHTKnRluolLpKKZWhlNpmrpuulAowt79FKfWj+fdgpZRWSt1ud7y7zXX3VdD7uduM2/7HppQaYcb1pN22nyilBns5vlTzd/3i4lFKTVVKHVFKbVdK7Tc/g9jCx7FbHqyU+sT8u7FSapX53v9QSpV7H9di8s2uQtu9ppQaZbfsr5Q6r5T6V6Htept9Lrebx3u8vGN28T60Uup9u+VRSqnX7JaHKaX2mj8blVI3mev9lFKblVId7bZdqpTq6424XVFKWc3PfZdSaoFSqrK5Pi+/vWm3bZRSKicv31Qku3Kjid26RkqpH808tlkptTIvvc38fq7Qed6sAuPPS/fdZh5+TillMV+zLzNrmO8pL58vrKiYC8XtkF/sXn9WKZWplKpkt+4WpVSSeb7uU0qtVkr19n70oJSqZvf5n1FKnbJbDnSRr64z32+gudxAKXVYKRVZTjHZp+lspVSsmxhL9RkopbrZ7Z9qfgbblHGNyM9r5rZ9lFI7zPJrp1KqT3m8x1Kmw3al1Bal1I3e+t8u4ikyEFk5uVYWl752+/3H/BzzzvFH7PbJNtN6m1LqnXKMv3C+CnWyvnCZv8tu/3bmubrPzA+TlVKhysfKUlFm7wBdlFIHgC7mcl55NxlAa20FRgE/K6V2YsxY8HlxB/W5iotSSgFzgFVa6wZa62bAy0AN4JDWuhXQEqgN9HNxmJ3AALvl/sB2z0VdPK31HK11q7wf4DNgDbAEo+ns6bwLlg9wF89orfU1QGNgK7CyhLF/BHxgpkFT4OPyCdfgJt+40xXYB/Qzj4MyKsWTgDvM99saY0YMb8gC7lFKRRV+wfwy9jhwk9a6CTAcmKmUijELgJHAp0qpAKXUAEBrrWd7KW5XMszPvQVwEXjC7rXDgP0XzL7Abm8GV4wBwK8Y5QdKqWDgJ4x+vg201m2AJ4F4u32+sz/XtdbF3jnysLx0b45x0egJ/MPJdm8Ay7TW15jnjaspK72luPwCxueyCbi70Po1WuvWWuvGwFPAJ8ruBpa3aK0v2JX1Eygo91pprbMplK/MfX4HVmNcwAE+BV7RWpfXYwfs0zQbuN9NjKX6DLTWS+yO9zsw0Fx+2H4npdQ1wHvAXWb5dSfwnlLq6nJ6n+7kva9rgDHAv9ztUAGKXCvdpa9ZWbkbOAF0BNBaT7Hb50/gVnO5PM/vwvlquJP1zvIPSqkawGzgRfOcbQosBiLMTXypLBVlYJaFt2utG5m/L5rrf9daD7HbbpnW+mqtdUut9WCzDHLJ5youwK1AjtZ6Qt4KrfU2jBMyb9kKbARii+4OGJWCduaXt3CgIbDNcyGXnFLqKuBV4CHABpzDmN96UHH7eVGJ4tGGD4AzQI8SHLcmRl/GvP13Xk6QTrjNN8UYAPwHY2BYe3NdBMYYsAvmsbK01vvKNWLXcjEqTc86ee1FjMrjeTOuLRjTDD5hLv8GrANeA97GyQWjgq3H8bzNAP5QSl1nLt8PzPJ6VIWY5UYH4DEKvmAOBNZrrefnbae13qW1nur9CEtHa30WY5Dl3/Iq53YKn5s7vBmbGw75RSnVAAgHxuJ4c8qBee6/AfzN0wGWhot8ledlYIhS6gUgQGv9jYfCWINxTSypMn0GLowC3tZaHwEwf/8LGF3K45SHSOBSBfxfd8pyrbwV2AWMp/SfSXlxla8Kl/l5nsAYvL0e8r9T/FdrneDBGH2M8vEf3+SLFZcWwObiNjDvfF6PUTt3RgPLgW4Y80jPd7GdV5l38WcCo7TWx+1eegd4XrmZAs6LShPPFqCJ263gA2CFUmqR2c2gsts9Sqe4fNPAvsmZgrtCKKVCgNuBH4FvMAt9887AfOCYUuobpdTAvCZ4L/kUGKjsusOYmlP0ff5urs8zBngGmKm1Pui5EEvHzE+3U/R8/Bbor5SqDVgx7g5WtD7AYq31fuCiUupajDTe4ma/+wt1bwjxeKQlpI3BjxaKTkn5KfCFMrq9vaKUquX96IpykV8GYJyna4DGSiln02vmKWnZ5E3O8hUAWutE4F2ML/IjPfHPlVL+GDeaSnTjqBw+g8JKUn55Uoh5Xu4FJgNvutuhApTlWpn3mcwBepvfNbzGVb4qpswH99/1fLYsFRXLFysuxWlgfvG8ABx3c2fwW4w7Wv0xTmhf8CawW2v9rf1K867TRuCBComqkFLG465ars1jTsFoCp6N8UCiDUqpoMsIszQO2Tc5Y3SNyNMbWKm1Tge+B+7Oq7CZTZm3Y6TFKOBLL8WL2UVkOkaXF3cUZjqbOgJJGBcGXxBid95WBZYVen0xRlemARhTIfqCARhlCObvIncxlVJzzD7cP9itLty9IcMbwZZCkfNVa70Eo7vb5xhf9LeqgoGSFaG4/NIf+FZrbQN+wOha6Iov3jJ0l696AAlAeffnz0vT3zFall3O2FNo+8v9DAorXFa5WucpeV2YmgDdgelOWiArVGmvlWZX7Z7AXPO68RtG92dvcJWv3JX5JeHrZamoIL5YcdkNtHHxWt4Yl4ZAe6XUna4OorXeiPHFLcq8u1WhlFK3APfiuuvC2xjdgHzlMylpPK2BP8y/MwqNd6kKnM9b0Fr/qbX+Umt9F0Z3qPL8Yl1cvinOAKCzUuooxt2fahjN7oDRTG92ieuC8fl504cYXUrC7Nbtoej7vNZcj1IqDPg/jJk5opVSPb0QpzsZ5nlbDwikUPc1sz/rZuB5jMpjhVJKVcNIv8lmvhiN0YVtN0ZaA6C1vhsYjJHPfZ5SKh6jRavIlJRa64ta65la64cwxi50LLyNFznNL+Y4iEbAMvNz6U/x3WLsy6YK5ypf2Y2r6w1Uwugp8G9lDnQuJxl2XwCfdNeHnPL7DArbDVxXaF1++eVNZhelKKAiK+lOlfJa2R0j3+w0P5Ob8F53MVf5qtgy31TWa/Zfh1K+/eOjfOVLsr0VQJBSamjeCqVUW4wTAMh/AudLGF1iijMGo99whVJKVQGmAA9rrVOcbaO13otReFfITDiFuYtHGZ7C6I+b12XvF+BB8/UQjMkTVprL3VXBLHAxGBWEU+UYstt84+Q9RGIU8nW11vW11vUxCtgBSqlws7KZpxVwrBzjdcvsrjYLo/KS5/+Ad80vQSilWmF8ef7MfP1VYJb5+Y0EPjC7VlY4rXUSRgvSKCddGd7HGKR5wfuRFXEfMF1rXc/MF3WAI8B+oEOhGybl+eXSY8wWlAnAJ1o7PnVYKXWbKpgNKAJogHH3tEI5yS8DgNfyzlWtdS0gVilV5Bw3v2D/HaMbnK9wla9uMsvL94EnzDEN8zAe4FahLuczcOE9YIxSqj6A+ftljPfuVcqY1c0PcxyjryjDtXIAMMTuGhYHdC3nim+ZuCnzPwEGKaWuz1uhlHrQfM9CuORzD6DUWmul1N3Ah0qpl4BM4ChGn317c4HXlFI3F3OsRR4LtHSGY/QrH1+oVbpwF7a3MGbq8hXO4vm3UurvGF/YNmDMVJJ3l+VpYKJZoVEYF+nV5mtdgf8opTLN5dFa6zPlFWgp8o29e4AVWussu3XzMCoHzwEvKKUmYgwgT8OoIHjb+9i10mmt5ytjCup1SikNpAAPauMBT80wZpa5xtx2m1JqCUbL2eveD70orfVWpdR2jDu1a+zW78a3ZhMrPGXo9xhdJ3sD45RSH2J06UkB/mm33f3KnJ7aNFJrvc6TwRYjr7tGAMZd26+AcU62a4MxA1cuxs2syVrrTd4L07VC+aU/RScCmWOu/w24WSm1FaNsOgs8pbX+2ZvxulFcvuqB0dUnr+XhNWCbUmqq1vqA90IsqhSfwbslONY2pdSLwALzi2wO8II2JlPwhrxzAoxr1CBtTPZTUUKVUiftlsdhzJhaomulWTnphjHTJABa6zSl1K/AHfhA19tiyvwEpVR/jFnlqmNMVrQao/sh+FZZKnyIKnTzTQghhBBCCOFB+twxn/4CrqLr+WR/MV/sKiaEEEIIIYQQDqTiIoQQQgghhPB5PjfGRQghhBBCiL82n+yJ5fOkxUUIIYQQQgjh86TiIoQQQgghhPB50lVMCCGEEEIIL1I+/JBHXyYtLkIIIYQQQgifJxUXIYQQQgghhM+TiosQQgghhBDC58kYFyGEEEIIIbxJxriUibS4CCGEEEIIIXyeVFyEEEIIIYQQPk+6igkhhBBCCOFV0lWsLKTFRQghhBBCCOHzpOIihBBCCCGE8HnSVUwIIYQQQghvklnFykRaXIQQQgghhBA+TyouQgghhBBCCJ8nXcWEEEIIIYTwJukqVibS4iKEEEIIIYTweVJxEUIIIYQQQvg86SomhBBCCCGEV0lXsbKQFhchhBBCCCGEz5OKixBCCCGEEMLnSVcxIYQQQgghvElmFSsTaXERQgghhBBC+DypuAghhBBCCCF8nlRchBBCCCGEED5PxrgIIYQQQgjhTTLEpUykxUUIIYQQQgjh86TiIoQQQgghhPB50lVMCCGEEEIIr5K+YmUhLS5CCCGEEEIInycVFyGEEEIIIYTPk65iQgghhBBCeJOSrmJlIS0uQgghhBBCCJ8nFRchhBBCCCGEz5OuYkIIIYQQQniTdBUrE2lxEUIIIYQQQvg8qbgIIYQQQgghfJ50FRNCCCGEEMKrpKtYWUiLixBCCCGEEMLnScVFCCGEEEII4fOk4iKEEEIIIYTweTLGRQghhBBCCG+S6ZDLRFpchBBCCCGEED5PKi5CCCGEEEIInyddxYQQQgghhPAm6SpWJtLiIoQQQgghhPB5UnERQgghhBBC+DzpKiaEEEIIIYRXSVexspAWFyGEEEIIIYTPk4qLEEIIIYQQwudJVzEhhBBCCCG8SWYVKxNpcRFCCCGEEEL4PKm4CCGEEEIIIXye0lpXdAxCCCGEEEIIUSxpcRFCCCGEEEL4PKm4CCGEEEIIIXyeVFyEEEIIIYQQPk8qLkIIIYQQQgifJxUXIYQQQgghhM+TiosQQgghhBDC5/0/1/vZMGPxYHsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing correlation on a heat map\n",
    "plt.figure(figsize=(15,15)) #decides the size of the image\n",
    "sns.heatmap(corr, cbar=True, square= True, fmt='.1f', annot=True, annot_kws={'size':15}, cmap='Reds', linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting target variable and independent variable\n",
    "X = data.drop(['PRICE'], axis = 1)\n",
    "Y = data['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting them to training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.3, random_state = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LINEAR REGRESSION\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing library\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#creating linear regressor\n",
    "lr = LinearRegression()\n",
    "\n",
    "#train the model using training set\n",
    "lr.fit(X_train, Y_train) #drop nan otherwise throw error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.550127507326124"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_ #value of y intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  0.7403225970999185\n",
      "MAE:  2.86611343196035\n",
      "MSE:  18.24409071946878\n",
      "RMSE: 4.271310187690514\n"
     ]
    }
   ],
   "source": [
    "#Evaluating model\n",
    "print('R^2: ', metrics.r2_score(Y_train,y_pred))\n",
    "# print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE: ', metrics.mean_absolute_error(Y_train,y_pred))\n",
    "print('MSE: ', metrics.mean_squared_error(Y_train, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(Y_train,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the actual price and the predicted prices\n",
    "\n",
    "plt.scatter(Y_train,y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\"Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking residual plot\n",
    "plt.scatter(y_pred, Y_train-y_pred)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.title(\"Checking for any pattern\")\n",
    "plt.show()\n",
    "\n",
    "#There is no pattern visible in this plot and values are distributed equally around zero. \n",
    "#So Linearity assumption is satisfied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Y_train-y_pred)\n",
    "plt.title(\"Histogram on residual\")\n",
    "plt.xlabel(\"Residual\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.show()\n",
    "\n",
    "#Here the residuals are normally distributed. So normality assumption is satisfied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### predicting test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  0.732036504130758\n",
      "MAE:  3.4301016596809273\n",
      "MSE:  25.180362809153582\n",
      "RMSE:  5.018003866992689\n"
     ]
    }
   ],
   "source": [
    "#model evaluation\n",
    "acc_linreg = metrics.r2_score(Y_test, y_test_pred)\n",
    "print(\"R^2: \", acc_linreg)\n",
    "print(\"MAE: \", metrics.mean_absolute_error(Y_test, y_test_pred))\n",
    "print(\"MSE: \", metrics.mean_squared_error(Y_test, y_test_pred))\n",
    "print(\"RMSE: \", np.sqrt(metrics.mean_squared_error(Y_test, y_test_pred)))\n",
    "\n",
    "#Here the model evaluations scores are almost matching with that of train data. So the model is not overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST REGRESSOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor()\n",
    "\n",
    "reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model predcition on train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  0.9746885272284593\n",
      "MAE:  0.8836202531645575\n",
      "MSE:  1.7783018481012638\n",
      "RMSE:  1.3335298452232944\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2: \", metrics.r2_score(Y_train, y_pred))\n",
    "print(\"MAE: \", metrics.mean_absolute_error(Y_train, y_pred))\n",
    "print(\"MSE: \", metrics.mean_squared_error(Y_train, y_pred))\n",
    "print(\"RMSE: \", np.sqrt(metrics.mean_squared_error(Y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the differences between actual prices and the predicted prices\n",
    "plt.scatter(Y_train, y_pred)\n",
    "plt.xlabel(\"Actual value\")\n",
    "plt.ylabel(\"Predicted value\")\n",
    "plt.title(\"Actual vs Predicted value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking on the residual\n",
    "plt.scatter(y_pred, Y_train-y_pred)\n",
    "plt.xlabel(\"predicted value\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.title(\"predicted vs residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### predicting the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 :  0.8904570787665496\n",
      "MAE:  2.216551470588233\n",
      "MSE:  10.293680080882345\n",
      "RMSE :  3.2083765491105223\n"
     ]
    }
   ],
   "source": [
    "acc_random = metrics.r2_score(Y_test, y_test_pred)\n",
    "print(\"R^2 : \", acc_random)\n",
    "print(\"MAE: \", metrics.mean_absolute_error(Y_test, y_test_pred))\n",
    "print(\"MSE: \", metrics.mean_squared_error(Y_test, y_test_pred))\n",
    "print(\"RMSE : \", np.sqrt(metrics.mean_squared_error(Y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb = XGBRegressor()\n",
    "\n",
    "xgb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9999989729380114\n",
      "MAE: 0.005883424795126592\n",
      "MSE: 7.215803872461283e-05\n",
      "RMSE: 0.008494588790789865\n"
     ]
    }
   ],
   "source": [
    "print('R^2:',metrics.r2_score(Y_train, y_pred))\n",
    "#print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(Y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(Y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(Y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#residual\n",
    "plt.scatter(Y_train, Y_train-y_pred)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.title(\"Predicted vs Residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicting test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8904570787665496\n",
      "MAE: 2.216551470588233\n",
      "MSE: 10.293680080882345\n",
      "RMSE: 3.2083765491105223\n"
     ]
    }
   ],
   "source": [
    "acc_xgb = metrics.r2_score(Y_test, y_test_pred)\n",
    "print('R^2:',acc_xgb)\n",
    "#print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(Y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(Y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(Y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svm_reg = svm.SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.6510940793642486\n",
      "MAE: 2.841175439488562\n",
      "MSE: 24.512996500845805\n",
      "RMSE: 4.951060139085952\n"
     ]
    }
   ],
   "source": [
    "print('R^2:',metrics.r2_score(Y_train, y_pred))\n",
    "#print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(Y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(Y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(Y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nh6TtopnUirmVap7Q+ISzx+6z2Xza0RlGn49e2FlcxMliueb4zWwA2AgcDPwz8Gtg1N13BTf5LRD5r2JmpwCnACxYsCDPYYrkImlDVrvSIWm6aLZ6UlVUR896ZVjM7ZWdxUWcLJZrVY+7T7j7YmA/4IXAH0XdLOZ7z3H3pe6+dP78+XkOUyQXScGwyHRIqydV1VcbRSnDYm6v7Cwu4mSxQqp63H3UzK4DjgSGzGx2MOvfD7iviDFIb+imt/JxM7fhocFCx9yOg1XCdxZxHUXLsJjbK4evF3EQTm5tmc1sPjAeBP1B4CqqC7vvAC5x9++a2b8Ct7j7vyTdl9oyC8QfCehUg2kRLwJxLzxR1yFda+WyvZg1Gk/ZxhuKO2x+eGiQ9auXd2BEnRfXljlT4DezWcCe7v5Yits+j+ri7QDVlNKF7v4JM3s2T5VzbgLe5u47ku5LgV8g/hc7lHe/+rjZ7glHDHPJxpHYvvpJLxQjo2Mzdr92su9+0hkBZQjuSbp57HlpOvCb2XeA9wITVBdq9wK+4O5n5THQKAr8AnDg6stjd4qG8pzdxb3wxG2MihtLmvbLtd9b5Ay722fNZX030imtHMTyXHd/zMzeClwBfJjqC0BhgV8E0jUxyzOfG3ffUUE/6va1s/y0j1V0pUq358l1VnA6aap6KmZWAVYA33f3cWIqcUTylLWJWbtlve+4hmpZvjdNpUqaPkDNjDnNdelOaQL/V4C7gD2An5rZAUDDHL9Iu2VtYtZuaV54QgYcdchTZchpDlkJ1f4cjWbgaTt0plVEKaF0XlNVPTXlmIVQjl+idCKfu27TCKdeeHNseqdW7cJio/WJuOqkpHWFSXdmZVxfSEN58t7RyuLuM4F/BPZ191eZ2XOBF7v7ufkMdSYFfimTNIvMoTAAJ1UkJZWiplkIjmLAnWuOzfQ9Eq9bXwzjAn+aVM83gSuBfYPP/x+wsn1DE2mfdua742TJd4cpmbgUyhdPWsz61ctjg0htestgqhd+O8coydqdTiuDNIF/H3e/EJgECFI82aYfIgUo6hc07iSqKGEArg/gWbpYrlgyzPrVy7lzzbFMpkwxKSffPr3SCqJWmnLObWb2dIJKnqC18qO5jkqkCc32vc8qakv9UYfMj9zEVRuA60sNw3cnWdIHcSWtYc6/m9IQ3aLbS1yjpAn8fw/8ADjIzNYD84E35DoqkSYU+QsaVS++9IC9U+eBm63Pj+qUmWV3arfmqjupiG6ZRWsY+N39JjN7KbCI6jvaO4JafpFS6fQvaJbNQ3HvTk7/wZbEwNxKA69eaVtctLgX225OpzUM/Gb2N8D57r4l+Hyemb25UWM1kaJ10y9o3LuQ0bFxRseq86q4wNzs7tS4F5tTL7x5xmPIU4rollm0NKmek939n8NP3P0RMzsZUOCXUinyFzSuG2fax07TfgLau0aR1HJCM/9kvdYKIk3gn2VmFhylGJ6qtVu+wxJpTl6/oLWBfmhuhSee3DV1wPjI6BirLroZrHoMYXgtKZimOdUqlLbNQyNJLzZ5LIJLeaUp57wSuNDM/szMlgMXAP+e77CkXxRRd9+q+jLRR7aPTwX90PikTwX9UFLJX1R556yYmtC0tfuNNGo50c1VKpJNmhn/h4H3AH9FdXH3KuBreQ5K+kMri41FVqdk6bNTLymY1r87Wbj68sjbpWkPkUb4WHEtJ7q5SkWySVPVMwl8OfgQaZtm6+7L0qo4jSzBdDjhqMZ2CZ+fblkEl3zEpnrM7MLgz1vN7Jb6j+KGKL2q2br7ondSpgnes4DKwPSUTNZgWlRnzFZ2EUtvSJrxvz/48zVFDET6T7N19+3eqNUobZRmIXZgwDjpBftz7daHWjrQHJKPamxXaqvXqlQkm9jA7+73BxU857r7ywsck/SJZuvu27lRK03aqD4gR7VCHp9wrt36UMvHE0YFZG28knZLrOpx9wlgu5ntVdB4pI80m3JoZ0okbdooTaO0dpVdNjtGkbTSVPU8CdxqZlcD28KL7v6+3EYlfaOZlEM7N2o1kzaKe8dhVGfncX31mx1vLzYJk85KE/gvDz5ESqPRC0baQNtM2mjVMYv4wNrNMw5jcYisSGo1VRM3xr0GKw2/V7pT3uXKDTdwufu3qG7a2gTcBFwQXBMppSx9+WvPxU1zHarBOq6yPmoW3mqqZtUxi6hE7O7atnNXKTe8SWuKOFeiYeA3s1cDvwb+Cfi/wK/M7FVtG4FISml3+WYJtNdufSjyPuKuh+Jq66PeKbSaqlmxZJg958x8cz4+4crz96Ai1nTSpHq+ABzl7r8CMLODqKZ+ftS2UYg0kCVdkiXQNhuUG1Uk1b5VjzsQPUsV0uj26E7oyvP3niLWdNL06nkwDPqB3wAPtm0EIilkmQXFBdSo61luWyupIqn+rXpU0M9ahdTsOKX7FPFvnWbGv8XMrgAupLp+dSLwCzM7HsDdL23baKTvpF3EyjILyrI/oJUe/nELzHG9fVo5HrGbzhqQ1hTxb50m8M8BHgBeGnz+ELA38FqqLwQK/NKUpPQNTC/X3GuwMnVASa3aWVDti8hegxXmVGYxun08MdCuWDLMhrsf5oIb7mXCnQEzTjiitV2tcS9Sk+7cuebYpu6zFw8DkWhF/FunadL2zrY9mkiNpOMHd+yanPaCUBkwKrNsWjvk+px67YvI6Ng4Brz1yAV8asVhsWNYt2mESzaOTKVjJty5ZOMISw/Yu+kzbPM6AlJtFvpH3v/WaXL8IrlIOn6w/gVhfMLZc85shmpq1+dUnvrvG/Ui4sD519+TWAYX9+Kzcu3mhucDRJXdrVy7mYe37Wi5YZtInhT4pWOyzoAf2T7Ojl2T0z4P65vjXkTCTVVxkiolRkbH+MDazSyMKR+Ny+WPjU+Cw7y5FXW/lFJKk+MXyUXcItacyiweiShfHDCLnZ0PxJRMQnPtF0LhPUaVjybd7/ikM3e32Wz6+NGxtxHplNjAb2Z/n/SN7v6F9g9H+kncItaGux/m/OvvmbY7drAykNgWOemUqqh3FmFufmR0DIPYnbi16g+JafSioRp7KaukGf/Tgj8XAS8AfhB8/lrgp3kOSvpH/SJWuNhaG4gNOOGIYa7d+lDmDphRufX6hWAPHiNN8K8N5o369KvGXsoqqR//GQBmdhXwfHd/PPj8dOCiQkYnPa++Kmb7zl2Ri7TnXX8PQ4MVKgM241DzesNDg4llcHELwUODlWnVRFFqg3l4v2dctmVGakqLuVJmaRZ3FwA7az7fCSzMZTTSV6KqYqJy+6HRsfGG0/LhoUHWr17O2SctZvvOXawMFmcXn3HV1OJsXArm0bHxqd24UH0XUCsqmK9YMsymjx/NF09arKMMpWukWdz9NnCjmX2P6q/d64F/y3VU0hfiqmKSjE967EKuUU2/rNs0wqqLb572zmB0bJxVF90MJLdirk09ZWmNqxp76SZpNnB92sx+BPxpcOmd7r6p0feZ2f5UXyD+EJgEznH3LwWpopOp7gAG+Ki7X9HM4KW7Nbv4OeE+Y7E33Ky1Yskwy9ZcE5kOGp+sdrNMuyVewVx6VdpyzrnAY+7+DTObb2YHuvudDb5nF3Cqu99kZk8DNganeAGc7e6fa3bQ0hviZt5DgxXMiE37DAez77hDyRtV2nSi/UHeB2uIZNEw8JvZacBSqtU93wAqwHnAsqTvc/f7gfuDvz9uZrcD+p8uU+KqYsYnJtlZs1GrVjgzj0rJrFy7ueFjhouz7ZrNpwnoOixdyibN4u7rgeMIztt19/t4qtQzFTNbCCwBbggu/a2Z3WJmXzezeTHfc4qZbTCzDQ89lHwohnSnsLXxUN0Rgtt2TkzryRMaMJuxaFq7QJxGOytt0p6UpMPSpWzSBP6d7u4E9RRmtkeWBzCzPYFLgJXu/hjwZeAgYDHVdwSfj/o+dz/H3Ze6+9L58+OPwZPutmLJMHvsni7jOOmeqjQzztBgpa0z7LQBXYelS9mk+Y270My+AgyZ2cnAXwJfS3PnZlahGvTPD/v2u/sDNV//KvDDzKOWnpI2AGY51rDeYGWA0487NNO4Gkkb0PPq1pkXrUf0vjSHrX8OuJhqAF8EfNzd/6nR95mZAecCt9e2dzCzZ9Xc7PXAbVkHLb0lbQCMStOk+d6hwUoudfVpT0padcwiBisD066VdYNXEQd9S+elOWz9M+5+tbuvcvcPuvvVZvaZFPe9DHg7sNzMNgcfrwY+a2a3mtktwFHAB1r7EaTbRQXGenFpmkbfu+ygvdl82tGxQT/tAe5pxx1XFhp3TGPZaD2iP6RJ9bwC+HDdtVdFXJvG3X/OzM2PAKrZl2lqyyujmqYlpWnqvzc0YMabX7R/w0NYWqm2yVIW2i17ArQe0R+SunP+FfDXwEHB7Dz0NOA/8x6Y9JcsO2ZrO2vWG86Qk06a3aYN0t0S0NPqtvUIaU7SjP87wI+AM4HVNdcfd/eHcx2V9LX6mXSYZgg3aCV1xMwya9fsdiYd6l4OeS+wJ3XnfBR41My+BDxc053zaWb2Ine/Ie57RbKo/09+1CHzuWTjSGQKJk35ZtpZu2a3M+lQ984rYsOfecIBFgBmtolqW+awjn8WsMHdn9+WEaSwdOlS37BhQ1EPJwWKmsGn7Y2fxIA71xyb+bEHKwOlXXiV/rBszTWxacz1q5dnui8z2+juS+uvp1ncNa95dXD3STPTkY3SFmdctiWyN36rhuZWWLbmmsRZq2a3UkZFpCDTBPDfmNn7qO64heqC72/aNgLpW+s2jST2329WZcB44sldU/ed9Fa51xZnpfsVkYJM07LhvcCfACPAb4EXAae0bQTSt/KoDR8eGmSP3WbP6PWjWnTpFkVs+EvTj/9B4E1te0TpG40qE9r51rUyYJz1hsNZsWSYA1dfHnmbfq7Wke5RRAoyqY7/Q+7+WTP7P0SkXd39fW0bhXSdNLX2jSoT4t7SNuOkF+zf8H77uVpHukveKcikVM/twZ8bgI0RH9Kn0vRzSbP1v1G7hd1nz4rc+h3lghvunWq7cNQh86kMTP/OyoCpFl0kkFTHf1nw57eKG450gzQ7XtNUJoS3jTtAZUfMYSxRwjN4R0bHWHvjvTPP5G1HqVAG6nAprejYBi4zu4yEXxd3P65to5Cukiaox6Vb9hqsTNUpxx2a3qqoQ1zC83aLCL46cUtaUcT/n6RUz+eoHpJyJzAGfDX4eAK1Uu5rcblyh6kOl1FpnMosY9vOXVMvCHkE/SRFLe6qw6W0ooj/P7GB391/4u4/AZa4+0nuflnw8RbgJW0bgXSdpNx8ODvZcPfDzKk89d9raLDCnnNmMz5RcM6lRlGLu+oBJK0oywau+Wb2bHf/DYCZHQjoLMQ+FtcKOTQ2PsF5198z7dq2nbsKC/qzgKjVgaMOyfbfttk8q6qKpBVl2cD1AeA6M7vOzK4DrgVWtm0E0pVWLBlm/erlqatuxic89W1btdfcSuT1a7c+lPo+WjmJqptO3JLyKcsGrn83s+cAhwSXtrr7jraNQEqt0aw3Sy1+EfP94aHBtrxVbqVXv3oASSs6uoErZGZzgb8HDnD3k83sOWa2yN11SHqPS1NdENW/vVMqA8a2HbtiX2CyvFVu9cVDPYCkFZ3cwBX6BrATeHHw+W+BT+U2IimNNNUF4XmyA9ZaIqfZ7w7PsZ03twIOo2PRTd+yvlVOe5C6SDdKE/gPcvfPAuMA7j5G87+n0mFZDhdPO+tdsWSYyRZKM+fNrfDWIxdEVgoZsMdu0RVEYX/yO9ccy9yIxmy1t8vaY195eullaQL/TjMbJEjRmtlBgHL8XSjrgmWWWW8rM+HRoH3ymccfVp2513Bg567JGS0Y6oNw3IuUAetXL8/8tjl8JxO+o2jmxUOkrNKcwPUK4GPAc4GrgGXAX7j7dbmPLqATuNoj68k+cSdUnXDEMNdufWjawhPQUq7fgLNPWhxbIjo0WGGP3Wdz3+gYQ3MruMOjY+NTRzVecENEm4aEn60oat0gnRR3AlfijN/MDNgKHA/8BXABsLTIoC/tk3XBMmrW+/wFe3H+9ffMeNcATN22GU78vgCo5u7Xr17O2Sct5snxSUbHxqce/7zr74kM+p1OzbRSEiqSp8SqHnd3M/NMbQIAABKISURBVFvn7kcA0U3OpWs0szGktrpg3aYRPrB284yqmXDBN5xZR90mjUZlocvWXMP2nbtSvasYMOt4aqaVklCRPKXZuXu9mb3A3X+R+2gkV1Gll41mxbWpillmsQE9fNdw1pV35Favn6V3/6R7x4OrWjdIWaUJ/EcB7zWzu4BtVNOx7u7Py3Ng0n5ZN4bU5/iTmqqF7xrKEtQGK7MaHraeN7VukLJKE/hflfsopC3SLCRm2RgSlaqIYjD1rmFobiWXA9Sz2j4+yfYg6HaqLXIz77BEipDUj38O1YPWDwZuBc51911FDUyyyaOHd9rZ+9zdBvjA2s2ccdkWHi0g6NdW+KRNK3Uit67WDVJWSTP+b1HdtPUzqrP+5wLvL2JQkk59/r0+FZMm2CW9S0jbh2fbzuqLTVEz/dOPO3RqjHElqlE6kYZS6wYpo6Ryzue6+9vc/SvAG4A/LWhMkkJ9qWBc/j0p2DUqN2x0Jm5ehocGY8tChwYr0wJp1BjjtpUrty5SlRT4p6ZvSvGUT9r8e1Kwa9SLJ6zjL1KYA49rmXD6cYdOuxaOsXbH75zKrIY7fUX6WVKq53Azeyz4uwGDwedhVc8f5D46iZUmbdEo2KU9ED1pY1U7DUfkwNPmx58cf+rolbHxSSqzjHlzK4xuH1duXaRObOB39+Lf40tqcfn3ATMm3VOVakatC4T3Xauo1svbd+7iA2s3c9aVd0yNPU2wjnrnMj7pzN1tNps+fnSmMajFgvSDNOWcUkJxpYJpdquGuf20bQ7qj1o08jlUJVwczlqR1K6NUnlURomUUZrunFJCrXSPjFsfSNPmwKjW6uetvu9/knb1zk9z/oBIL9CMv4s1WyoYNxOOa3NQPxMuqmwzHGej9Eu7NkqpxYL0i9wCv5ntD/wb8IfAJHCOu3/JzPYG1gILgbuAN7r7I3mNQ2bK2kogbQVRu+07NMjH1t3K+dffM5Vaikq/NLtRqv4FJW7XscpApdfkOePfBZzq7jeZ2dOAjWZ2NdX2zj929zVmthpYDXw4x3FInawz5CIqeioDxvjEUysHg5UBjjpk/rSgH4ramJb13U9UPr8yyyLHoTJQ6TW5BX53vx+4P/j742Z2OzAMvA54WXCzbwHX0eeBv+hKkqwz5IGY6p922nP32dMOV1l1zKLETp+tpl/iKoFq20Goqkd6VSE5fjNbCCwBbgCeGbwo4O73m9kzYr7nFOAUgAULFhQxzI7oVCVJmhly+IKUd9CH6rrBYGWAs09aPDWulWs3x96+1fRL3AvHo2PjbD4tWwmoSLfJvarHzPYELgFWuvtjjW4fcvdz3H2puy+dP39+fgPssLJWktS2c2incGNVlNqfu9EpVa2mX9pVCSTSjXIN/GZWoRr0z3f3S4PLD5jZs4KvPwt4MM8xlF1ZK0nauaA7NFiZKjk968TD2fTxo2P76dw3Osa6TSOceuHNiffZ6ruhuJYQyudLP8izqseAc4Hb3f0LNV/6AfAOYE3w5/fzGkM3KOthHe184dlj99nTOmpC/M89NLcSu7ks1Oy5vrXUMln6WZ45/mXA24FbzSxM1n6UasC/0MzeBdwDnJjjGEqvrId1pG3JnEbUukXcz+1O4juN2kNfWqWWydKvckv1uPvP3d3c/Xnuvjj4uMLdf+/uf+buzwn+fDivMXSDVnbg5mnVMYti0zHNqF+3iPu5Hx2L3xxmwFuPXNDx50ak22nnbgkUOfMMK3VGRsemyjSjumKuWDLMhrsfjqyjb1aYv09Kr8R1Ah0w4/NvPFxBX6QNzAso1WvV0qVLfcOGDZ0eRterLx2tVZll7Dln9ow2xvWBevvOXU23bJg3t8KT45OJjeWixpi2+ZyITGdmG919af11zfh7WFTQjsufj096YnfM8H72GqzM2N2aVlT+vn4XrhZdRfLf1KkZf0m1+g+fNLtPa8CMN79ofy7ZODLtfiqzqimiyYz/deLaORtw55pjmx6nSC9p57veuBm/2jKXUKOzcNNoRx3+hDvnXX9PZGuDSZ95tm2j83njSlRnmXHg6stZtuaaTD+jSC8qYlOnAn8JteMfvogNYLWz98HKrBln39aaN7cSe3j7hHvTL3AivaaITZ0K/CUUVz+f5R8+bnY9NFiZ2gA1YDZ1rf5w8qzGxif5yKW3RC78VgaM01576IwSzvDxp99P59tViHRSEe1EtLhbMus2jcTmwrP8w8dtkKrfQVv7uKdeeHNLDdnGag48D3+G+lLR2tLVA1dfHnk/nW5XIdJJRWzqVOAvmbhWxFl3rGatjpnaUXvRzYxnXbWNEAb99auXx96mrO0qRDqpiMo2Bf6SiZvthqF42ZprUv9nyLoxbMWSYc64bEvbjlZsNHMva7sKkU7Le1OnAn/JxM2C5wXNy/Lu2z/axvN0G83cVbMv0hkK/CWTpXlZ7eantK0YkqzbNBJfbF8jxU2mfpZG6mc26zaNZHpXIyLZqaqnZLI2Lwv739QemhIu0GYpjwzvo9HabtgorVFr5HlzK5kDdjv2L4hIY9q52yUWn3EVoxHBPzwjNqmFcqNF1rQVPcN1PXziFoKb3WW4bM01kT9Ho/GLSDT16umAdZtGpi2WDg1WYsspG4koeQdgfGKy4SJq0tfDWXaaoD8j+MaMaffZzb2RLOtpZCK9RoE/J+s2jbDq4punNTMbHRtn1UXVIwWzBv+4RddtOyeYN7eSWImTtMiaprVDWGlT2z9oVrCOEDnWsfGmFp5V3ilSDOX4c3LWlXdEdrAcn/SmdqYmBT/3+D45jcoj05yydebxhwFMy783eofQzA5cnYMrUoy+Dfxh9UhezcGS0hPh17KMISn4PTo2zglHDM/IvBhwwhHx9cDhLuEk4SJtM03fsqZoynoamUiv6dnF3aS2xkUc9hG3UAlPLZLWjyGuzUEoboE3yYAZk+6RpZFJYwwNDVbYfNrRHLj68swncWlRVqSz+qotc6OywCLanq46ZlFk47PKLGPVMYsixxAG1rgyxtOPO7Rh6+N6tZ0vV11887T7TDMjD8tI41JNYaO1qBbNStGIlFNPBv5Ggb2I6pEVS4Y56w2HT2tTPDRY4awTq+fGNnqsqBeiMBXSbB/N8QnnjMu2TH2eZtE0vE1c/v3zbzycu9Ycy9knLVaKRqRL9GRVT6PAXlT1SFK/jbgx1GrUt6cZj2wfZ9maa1h1zKLIdFOtyoCxbccuDlx9OfsODXLCEcNcu/WhyPRZkQfGi0hrenLG36ifdRmqR+IOJalV/3OEG61aVdvnp3Yxdd7cCkODlam/49XSzDBVdMnGEVYds4g71xzL+tXLFehFulRPLu6mWbzN+zDjtOMM++vU978JxwvE3iZKZcA46w2Hs3Lt5oaPn7T4ql20It2vr3bupun6WIbURO0Yol6IgGkvYKleooMbDQ1WGlYApSk5zfI9ItIdejLwQzkCexZR41225prMtfPhBrHTjzu04aEqSWsa2kUr0rt6MsffK5qdXd83OlatKjrx8KkumlnLLcuwDiIi+ejZGX+Z1Tdvm1uZxW6zB3h0bHxaWqpR5U+js3kbpZIand4FOiRFpBf15OJumUU1b6tXu7AbV245YMaRz57HjXc9Mu2+wsVdBWgR6avF3TKLa95Wa2x8gtN/sIU9dp/N2PjE1IlatSbc+a/fPDxzyl/+13ER6TDl+AuWNm8/OjY+40StepMOk3XXmu3+KSL9QzP+gqXZsduqPEsuy7D/QURaoxl/weKat7XTLLNczqnVmbgivUGBv2BRzdvabcI9l4BcRFdTEcmfUj1tlDYNEl5LapDWqjAgtzMNo928Ir1Bgb9N6vsD1TZCiwq+zZxolVWjQ9az5uq1m1ekNyjV0yZxaZCVazdHHqtYxCw5LiA3m6vXbl6R3pBb4Dezr5vZg2Z2W821081sxMw2Bx+vzuvxs2jH+btJgTwqsDY60SpO2mXhpIDcbK5eZ+KK9IY8Z/zfBF4Zcf1sd18cfFyR4+On0q5KlUbpjvrAGjd7fvOL9p9xPQz2w0ODUyddRRkwSxWQW8nVr1gyzPrVy9WTX6SL5Zbjd/efmtnCvO6/XZJmv1mCWqPTrGB6YF2xZJgNdz/MBTfcy4Q7A2accMQwn1pxGEsP2Lth/r2Vw+KVqxfpb51Y3P1bM/tzYANwqrs/EnUjMzsFOAVgwYIFuQ2mXZUqtU3N4jZo1QbWdZtGuGTjyNSu3Al3Ltk4wtID9m7YUrrVBmpRL1LK1Yv0j6ID/5eBT1LtKPNJ4PPAX0bd0N3PAc6BapO2vAbUztlvGHijmrBVZtm0wNrqO41WzhtQ502R/lZoVY+7P+DuE+4+CXwVeGGRjx+l3ZUqcU3Y9pwze1pgjXtHMTI6xsIWFplFRBopdMZvZs9y9/uDT18P3JZ0+yK0e/YbF9BHt08/BrFRz55G+wBakXXPgYj0ltwCv5ldALwM2MfMfgucBrzMzBZTTfXcBbwnr8fPop3HNKZNHaVZDM5j9y20b0FbRLpTnlU9b464fG5ej1cWaRdOwwC7cu3mxPvLY6OXWi+I9Dft3G2zLJucViwZjq3JD+VRYhl3nyrnFOkP6tWTgyypo6SUT14llirnFOlvCvwdVl//Hx6zOJxjiaXKOUX6mw5bFxHpUXGHrSvHLyLSZxT4RUT6jAK/iEifUeAXEekzCvwiIn2mK6p6zOwh4O5Oj6PGPsDvOj2IktJzE0/PTTw9N9FafV4OcPf59Re7IvCXjZltiCqREj03SfTcxNNzEy2v50WpHhGRPqPALyLSZxT4m3NOpwdQYnpu4um5iafnJlouz4ty/CIifUYzfhGRPqPALyLSZxT4GzCzr5vZg2Z2W821vc3sajP77+DPeZ0cY6eY2f5mdq2Z3W5mW8zs/cH1vn5+zGyOmd1oZjcHz8sZwfUDzeyG4HlZa2a7dXqsnWJmA2a2ycx+GHyu5wYws7vM7FYz22xmG4Jrbf99UuBv7JvAK+uurQZ+7O7PAX4cfN6PdgGnuvsfAUcCf2Nmz0XPzw5gubsfDiwGXmlmRwKfAc4OnpdHgHd1cIyd9n7g9prP9dw85Sh3X1xTv9/23ycF/gbc/afAw3WXXwd8K/j7t4AVhQ6qJNz9fne/Kfj741R/kYfp8+fHq54IPq0EHw4sBy4Orvfd8xIys/2AY4GvBZ8bem6StP33SYG/Oc909/uhGvyAZ3R4PB1nZguBJcAN6PkJUxmbgQeBq4FfA6Puviu4yW+pvkj2oy8CHwImg8+fjp6bkANXmdlGMzsluNb23ycdvSgtM7M9gUuAle7+WHUC19/cfQJYbGZDwPeAP4q6WbGj6jwzew3woLtvNLOXhZcjbtp3z01gmbvfZ2bPAK42s615PIhm/M15wMyeBRD8+WCHx9MxZlahGvTPd/dLg8t6fgLuPgpcR3UNZMjMwsnWfsB9nRpXBy0DjjOzu4DvUk3xfBE9NwC4+33Bnw9SnTC8kBx+nxT4m/MD4B3B398BfL+DY+mYIDd7LnC7u3+h5kt9/fyY2fxgpo+ZDQIvp7r+cS3whuBmffe8ALj7R9x9P3dfCLwJuMbd34qeG8xsDzN7Wvh34GjgNnL4fdLO3QbM7ALgZVTboz4AnAasAy4EFgD3ACe6e/0CcM8zs5cAPwNu5al87Uep5vn79vkxs+dRXYQboDq5utDdP2Fmz6Y6y90b2AS8zd13dG6knRWkej7o7q/RcwPBc/C94NPZwHfc/dNm9nTa/PukwC8i0meU6hER6TMK/CIifUaBX0Skzyjwi4j0GQV+EZE+o8AvApjZRNAR8TYzu8jM5sbc7oqwRl+kW6mcUwQwsyfcfc/g7+cDG2s3pQWb1czdJ+PuQ6RbaMYvMtPPgIPNbGFw1sC/ADcB+wf90vcBMLM/N7Nbgr773w6uzTezS8zsF8HHsuD6S4N3FJuDPvRP69hPJ31PTdpEagT9Yl4F/HtwaRHwTnf/6+Dr4e0OBf6BalOt35nZ3sHtv0S1r/zPzWwBcCXVBm0fBP7G3dcHTe2eLOpnEqmnwC9SNRi0UYbqjP9cYF/gbne/PuL2y4GL3f13ADVb6F8OPLemQ+kfBLP79cAXgjTSpe7+25x+DpGGFPhFqsbcfXHthSB4b4u5vRHdOngW8GJ3H6u7vsbMLgdeDVxvZi9391xa7oo0ohy/SHN+DLwxaKBFTarnKuBvwxuZ2eLgz4Pc/VZ3/wywATik4PGKTFHgF2mCu28BPg38xMxuBsIKoPcBS4NF318C7w2urwxKRW8GxoAfFT5okYDKOUVE+oxm/CIifUaBX0Skzyjwi4j0GQV+EZE+o8AvItJnFPhFRPqMAr+ISJ/5/3Ah5RJC3ZkGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_pred,Y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for Test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = svm_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.5425335609760945\n",
      "MAE: 3.7719750353519044\n",
      "MSE: 42.98783634788254\n",
      "RMSE: 6.556510988924105\n"
     ]
    }
   ],
   "source": [
    "acc_svm = metrics.r2_score(Y_test, y_test_pred)\n",
    "print('R^2:', acc_svm)\n",
    "#print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(Y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(Y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(Y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation and Comparison of all the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>R-squared Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>89.045708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>89.045708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>73.203650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>54.253356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  R-squared Score\n",
       "1            Random Forest        89.045708\n",
       "2                  XGBoost        89.045708\n",
       "0        Linear Regression        73.203650\n",
       "3  Support Vector Machines        54.253356"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Linear Regression', 'Random Forest', 'XGBoost', 'Support Vector Machines'],\n",
    "    'R-squared Score': [acc_linreg*100, acc_random*100, acc_xgb*100, acc_svm*100]})\n",
    "models.sort_values(by='R-squared Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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